{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learn Keras for Deep Neural Networks\n",
    "## Chapter 5 - Tuning and Deploying Deep Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L1 Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import regularizers\n",
    "from keras import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras import Sequential\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(256, input_dim=128, kernel_regularizer=regularizers.l1(0.01)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L2 Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(256, input_dim=128, kernel_regularizer=regularizers.l2(0.01)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropout Regularization"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import Sequential\n",
    "from keras.layers.core import Dropout, Dense\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(100, input_dim= 50, activation='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Dense(1,activation=\"linear\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Optimization : Weight & Bias Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import Sequential\n",
    "from keras.layers import Dense\n",
    "model = Sequential()\n",
    "model.add(Dense(64,activation=\"relu\", input_dim = 32, kernel_initializer = \"random_uniform\",bias_initializer = \"zeros\"))\n",
    "model.add(Dense(1,activation=\"sigmoid\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search using Keras Wrapper for Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "Epoch 1/30\n",
      "Epoch 1/15\n",
      "Epoch 1/15\n",
      "666/666 [==============================] - 0s 648us/step - loss: 0.6967 - acc: 0.5195\n",
      " 32/667 [>.............................] - ETA: 8s - loss: 0.6962 - acc: 0.4688Epoch 2/15\n",
      "666/666 [==============================] - 0s 47us/step - loss: 0.6957 - acc: 0.5135\n",
      "Epoch 3/15\n",
      "666/666 [==============================] - 0s 46us/step - loss: 0.6949 - acc: 0.4970\n",
      "Epoch 4/15\n",
      "667/667 [==============================] - 0s 728us/step - loss: 0.6954 - acc: 0.5037\n",
      "667/667 [==============================] - 0s 729us/step - loss: 0.6940 - acc: 0.5142\n",
      "Epoch 2/15\n",
      "Epoch 2/15\n",
      "666/666 [==============================] - 1s 773us/step - loss: 0.6967 - acc: 0.5195\n",
      "Epoch 2/30\n",
      "666/666 [==============================] - 0s 45us/step - loss: 0.6957 - acc: 0.5135\n",
      "667/667 [==============================] - 0s 66us/step - loss: 0.6949 - acc: 0.5052\n",
      "Epoch 3/30\n",
      "Epoch 3/15\n",
      "666/666 [==============================] - 0s 43us/step - loss: 0.6949 - acc: 0.4970\n",
      "224/667 [=========>....................] - ETA: 0s - loss: 0.6998 - acc: 0.4777Epoch 4/30\n",
      "667/667 [==============================] - 0s 59us/step - loss: 0.6946 - acc: 0.5127\n",
      "Epoch 4/15\n",
      "666/666 [==============================] - 0s 43us/step - loss: 0.6942 - acc: 0.5030\n",
      "Epoch 5/30\n",
      "667/667 [==============================] - 0s 185us/step - loss: 0.6933 - acc: 0.5307\n",
      "Epoch 3/15\n",
      "666/666 [==============================] - 0s 256us/step - loss: 0.6942 - acc: 0.5030\n",
      "Epoch 5/15\n",
      "667/667 [==============================] - 0s 126us/step - loss: 0.6941 - acc: 0.5142\n",
      "667/667 [==============================] - 0s 60us/step - loss: 0.6929 - acc: 0.5247\n",
      "Epoch 4/15\n",
      "666/666 [==============================] - 0s 60us/step - loss: 0.6935 - acc: 0.5030\n",
      "Epoch 6/15\n",
      "Epoch 5/15\n",
      "667/667 [==============================] - 0s 34us/step - loss: 0.6925 - acc: 0.5232\n",
      "Epoch 5/15\n",
      "666/666 [==============================] - 0s 51us/step - loss: 0.6927 - acc: 0.5090\n",
      "Epoch 7/15\n",
      "666/666 [==============================] - 0s 234us/step - loss: 0.6935 - acc: 0.5030\n",
      "Epoch 6/30\n",
      "667/667 [==============================] - 0s 70us/step - loss: 0.6942 - acc: 0.5172\n",
      "Epoch 6/15\n",
      "666/666 [==============================] - 0s 65us/step - loss: 0.6923 - acc: 0.5060\n",
      "Epoch 8/15\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6927 - acc: 0.5090\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6938 - acc: 0.5052\n",
      "Epoch 7/15\n",
      "666/666 [==============================] - 0s 49us/step - loss: 0.6916 - acc: 0.5150\n",
      "666/666 [==============================] - 0s 38us/step - loss: 0.6923 - acc: 0.5060\n",
      "Epoch 9/15\n",
      "Epoch 8/30\n",
      "667/667 [==============================] - 0s 156us/step - loss: 0.6923 - acc: 0.5217\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.6934 - acc: 0.5112\n",
      "Epoch 8/15\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6994 - acc: 0.4688Epoch 6/15\n",
      "666/666 [==============================] - 0s 43us/step - loss: 0.6916 - acc: 0.5150\n",
      "Epoch 9/30\n",
      "666/666 [==============================] - 0s 54us/step - loss: 0.6912 - acc: 0.5225\n",
      "Epoch 10/15\n",
      "667/667 [==============================] - 0s 65us/step - loss: 0.6934 - acc: 0.5157\n",
      "667/667 [==============================] - 0s 36us/step - loss: 0.6922 - acc: 0.5202\n",
      "Epoch 7/15\n",
      "666/666 [==============================] - 0s 41us/step - loss: 0.6906 - acc: 0.5255\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6896 - acc: 0.5312Epoch 11/15\n",
      "666/666 [==============================] - 0s 62us/step - loss: 0.6912 - acc: 0.5225\n",
      "Epoch 9/15\n",
      "Epoch 10/30\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6903 - acc: 0.5315\n",
      "Epoch 12/15\n",
      "667/667 [==============================] - 0s 51us/step - loss: 0.6919 - acc: 0.5217\n",
      "Epoch 8/15\n",
      "666/666 [==============================] - 0s 51us/step - loss: 0.6906 - acc: 0.5255\n",
      "Epoch 11/30\n",
      "666/666 [==============================] - 0s 45us/step - loss: 0.6901 - acc: 0.5360\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6919 - acc: 0.5202\n",
      "667/667 [==============================] - 0s 77us/step - loss: 0.6930 - acc: 0.5172\n",
      "Epoch 13/15\n",
      "Epoch 9/15\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6844 - acc: 0.5312Epoch 10/15\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6916 - acc: 0.5247\n",
      "Epoch 10/15\n",
      "666/666 [==============================] - 0s 52us/step - loss: 0.6895 - acc: 0.5315\n",
      "Epoch 14/15\n",
      "666/666 [==============================] - 0s 74us/step - loss: 0.6903 - acc: 0.5315\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6929 - acc: 0.5262\n",
      "Epoch 11/15\n",
      "667/667 [==============================] - 0s 67us/step - loss: 0.6917 - acc: 0.5202\n",
      "667/667 [==============================] - 0s 36us/step - loss: 0.6933 - acc: 0.5067\n",
      "Epoch 12/15\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6922 - acc: 0.5938Epoch 11/15\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6929 - acc: 0.5337\n",
      "Epoch 13/15\n",
      "666/666 [==============================] - 0s 72us/step - loss: 0.6893 - acc: 0.5480\n",
      "666/666 [==============================] - 0s 106us/step - loss: 0.6901 - acc: 0.5360\n",
      "Epoch 15/15\n",
      "Epoch 13/30\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6925 - acc: 0.5217\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6844 - acc: 0.5312Epoch 14/15\n",
      "666/666 [==============================] - 0s 46us/step - loss: 0.6895 - acc: 0.5315\n",
      "Epoch 14/30\n",
      "667/667 [==============================] - 0s 110us/step - loss: 0.6918 - acc: 0.5172\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6882 - acc: 0.5312Epoch 12/15\n",
      "666/666 [==============================] - 0s 61us/step - loss: 0.6890 - acc: 0.5450\n",
      "666/666 [==============================] - 0s 41us/step - loss: 0.6893 - acc: 0.5480\n",
      "Epoch 15/30\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6913 - acc: 0.5262\n",
      "Epoch 13/15\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6911 - acc: 0.5172\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6890 - acc: 0.5450\n",
      "Epoch 16/30\n",
      "Epoch 14/15\n",
      "667/667 [==============================] - 0s 135us/step - loss: 0.6922 - acc: 0.5112\n",
      "Epoch 15/15\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6890 - acc: 0.5420\n",
      "Epoch 17/30\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6912 - acc: 0.5112\n",
      "Epoch 15/15\n",
      "667/667 [==============================] - 0s 50us/step - loss: 0.6921 - acc: 0.5112\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6886 - acc: 0.5435\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6908 - acc: 0.5172\n",
      "Epoch 18/30\n",
      "334/334 [==============================] - 0s 374us/steps: 0.6827 - acc: 0.59\n",
      "666/666 [==============================] - 0s 38us/step - loss: 0.6885 - acc: 0.5420\n",
      "Epoch 19/30\n",
      "666/666 [==============================] - 0s 18us/stepss: 0.6813 - acc: 0.62\n",
      "666/666 [==============================] - 0s 38us/step - loss: 0.6884 - acc: 0.5360\n",
      "Epoch 20/30\n",
      "333/333 [==============================] - 0s 259us/steps: 0.6759 - acc: 0.62\n",
      "666/666 [==============================] - 0s 37us/step - loss: 0.6880 - acc: 0.5375\n",
      "Epoch 21/30\n",
      "667/667 [==============================] - 0s 19us/stepss: 0.6920 - acc: 0.50\n",
      "333/333 [==============================] - 0s 286us/step\n",
      "666/666 [==============================] - 0s 36us/step - loss: 0.6879 - acc: 0.5300\n",
      " 32/667 [>.............................] - ETA: 0sEpoch 22/30\n",
      "667/667 [==============================] - 0s 22us/stepss: 0.6741 - acc: 0.59\n",
      "666/666 [==============================] - 0s 61us/step - loss: 0.6877 - acc: 0.5450\n",
      "Epoch 23/30\n",
      "666/666 [==============================] - 0s 38us/step - loss: 0.6876 - acc: 0.5435\n",
      "Epoch 24/30\n",
      "666/666 [==============================] - 0s 38us/step - loss: 0.6879 - acc: 0.5435\n",
      "Epoch 25/30\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6874 - acc: 0.5465\n",
      "Epoch 26/30\n",
      "666/666 [==============================] - 0s 41us/step - loss: 0.6872 - acc: 0.5495\n",
      "Epoch 27/30\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6872 - acc: 0.5495\n",
      "Epoch 28/30\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6869 - acc: 0.5450\n",
      "Epoch 29/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "666/666 [==============================] - 0s 39us/step - loss: 0.6868 - acc: 0.5405\n",
      "Epoch 30/30\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6867 - acc: 0.5465\n",
      "334/334 [==============================] - 0s 222us/step\n",
      "666/666 [==============================] - 0s 18us/step\n",
      "Epoch 1/30\n",
      "Epoch 1/30\n",
      "Epoch 1/60\n",
      "Epoch 1/60\n",
      "667/667 [==============================] - 0s 624us/step - loss: 0.7108 - acc: 0.5232\n",
      "Epoch 2/30\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.7038 - acc: 0.5217\n",
      "Epoch 3/30\n",
      "666/666 [==============================] - 0s 636us/step - loss: 0.7184 - acc: 0.5120\n",
      "Epoch 2/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6994 - acc: 0.5232\n",
      "Epoch 4/30\n",
      "666/666 [==============================] - 0s 49us/step - loss: 0.7084 - acc: 0.5105\n",
      "667/667 [==============================] - 0s 46us/step - loss: 0.6980 - acc: 0.5157\n",
      "Epoch 3/60\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.7383 - acc: 0.4688Epoch 5/30\n",
      "666/666 [==============================] - 0s 54us/step - loss: 0.7030 - acc: 0.5060\n",
      "Epoch 4/60\n",
      "667/667 [==============================] - 0s 68us/step - loss: 0.6962 - acc: 0.4903\n",
      "Epoch 6/30\n",
      "667/667 [==============================] - 1s 825us/step - loss: 0.7137 - acc: 0.5247\n",
      "Epoch 2/30\n",
      "667/667 [==============================] - 0s 36us/step - loss: 0.7063 - acc: 0.5232\n",
      "Epoch 3/30\n",
      "667/667 [==============================] - 0s 34us/step - loss: 0.7020 - acc: 0.5247\n",
      "Epoch 4/30\n",
      "667/667 [==============================] - 0s 131us/step - loss: 0.6955 - acc: 0.4843\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 0s 34us/step - loss: 0.6994 - acc: 0.5172\n",
      "Epoch 5/30\n",
      "666/666 [==============================] - 0s 191us/step - loss: 0.6999 - acc: 0.4685\n",
      "Epoch 5/60\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6982 - acc: 0.4565\n",
      "Epoch 6/60\n",
      "667/667 [==============================] - 0s 76us/step - loss: 0.6950 - acc: 0.4858\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6881 - acc: 0.5625Epoch 8/30\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6972 - acc: 0.4535\n",
      "Epoch 7/60\n",
      "667/667 [==============================] - 0s 63us/step - loss: 0.6947 - acc: 0.4978\n",
      "Epoch 9/30\n",
      "666/666 [==============================] - 0s 41us/step - loss: 0.6965 - acc: 0.4550\n",
      "Epoch 8/60\n",
      "666/666 [==============================] - 0s 54us/step - loss: 0.6958 - acc: 0.4474\n",
      "667/667 [==============================] - 0s 225us/step - loss: 0.6980 - acc: 0.4873\n",
      "667/667 [==============================] - 0s 62us/step - loss: 0.6945 - acc: 0.4933\n",
      "Epoch 10/30\n",
      "Epoch 6/30\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.7088 - acc: 0.4062Epoch 9/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6969 - acc: 0.4828\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 0s 47us/step - loss: 0.6942 - acc: 0.4933\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6696 - acc: 0.5625Epoch 11/30\n",
      "666/666 [==============================] - 0s 55us/step - loss: 0.6954 - acc: 0.4535\n",
      "Epoch 10/60\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.6962 - acc: 0.4948\n",
      "Epoch 8/30\n",
      "667/667 [==============================] - 0s 44us/step - loss: 0.6940 - acc: 0.5082\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6958 - acc: 0.4918\n",
      "Epoch 9/30\n",
      "666/666 [==============================] - 0s 56us/step - loss: 0.6950 - acc: 0.4625\n",
      "667/667 [==============================] - 0s 48us/step - loss: 0.6940 - acc: 0.5112\n",
      "Epoch 11/60\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6880 - acc: 0.5312Epoch 13/30\n",
      "666/666 [==============================] - 0s 45us/step - loss: 0.6946 - acc: 0.4730\n",
      "Epoch 12/60\n",
      "667/667 [==============================] - 0s 67us/step - loss: 0.6937 - acc: 0.5112\n",
      "Epoch 14/30\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6944 - acc: 0.4805\n",
      "Epoch 13/60\n",
      "667/667 [==============================] - 0s 126us/step - loss: 0.6955 - acc: 0.5022\n",
      "Epoch 10/30\n",
      "666/666 [==============================] - 0s 43us/step - loss: 0.6942 - acc: 0.4865\n",
      "Epoch 14/60\n",
      "667/667 [==============================] - 0s 86us/step - loss: 0.6949 - acc: 0.5022\n",
      "667/667 [==============================] - 0s 119us/step - loss: 0.6936 - acc: 0.5112\n",
      "Epoch 11/30\n",
      "Epoch 15/30\n",
      "667/667 [==============================] - 0s 50us/step - loss: 0.6945 - acc: 0.5022\n",
      "Epoch 12/30\n",
      "666/666 [==============================] - 0s 128us/step - loss: 0.6937 - acc: 0.4910\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.6967 - acc: 0.4993\n",
      "Epoch 2/60\n",
      "608/667 [==========================>...] - ETA: 0s - loss: 0.6940 - acc: 0.5049Epoch 15/60\n",
      "667/667 [==============================] - 0s 59us/step - loss: 0.6943 - acc: 0.5082\n",
      "667/667 [==============================] - 0s 51us/step - loss: 0.6955 - acc: 0.5112\n",
      "Epoch 13/30\n",
      "Epoch 3/60\n",
      "667/667 [==============================] - 0s 129us/step - loss: 0.6935 - acc: 0.5097\n",
      "Epoch 16/30\n",
      "666/666 [==============================] - 0s 66us/step - loss: 0.6935 - acc: 0.5000\n",
      "Epoch 16/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6933 - acc: 0.5217\n",
      "Epoch 17/30\n",
      "666/666 [==============================] - 0s 41us/step - loss: 0.6932 - acc: 0.5120\n",
      "Epoch 17/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6933 - acc: 0.5157\n",
      "Epoch 18/30\n",
      "666/666 [==============================] - 0s 45us/step - loss: 0.6931 - acc: 0.5105\n",
      "Epoch 18/60\n",
      "667/667 [==============================] - 0s 150us/step - loss: 0.6940 - acc: 0.5067\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6934 - acc: 0.5262\n",
      "Epoch 19/30\n",
      "666/666 [==============================] - 0s 42us/step - loss: 0.6929 - acc: 0.5135\n",
      "Epoch 19/60\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6916 - acc: 0.5938Epoch 14/30\n",
      "667/667 [==============================] - 0s 243us/step - loss: 0.6949 - acc: 0.5202\n",
      "Epoch 4/60\n",
      "667/667 [==============================] - 0s 46us/step - loss: 0.6938 - acc: 0.5082\n",
      "Epoch 15/30\n",
      "667/667 [==============================] - 0s 69us/step - loss: 0.6932 - acc: 0.5292\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6945 - acc: 0.5202\n",
      "Epoch 5/60\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6980 - acc: 0.5312Epoch 20/30\n",
      "667/667 [==============================] - 0s 49us/step - loss: 0.6937 - acc: 0.4933\n",
      "Epoch 16/30\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6942 - acc: 0.5157\n",
      "Epoch 6/60\n",
      "667/667 [==============================] - 0s 48us/step - loss: 0.6933 - acc: 0.5097\n",
      "Epoch 17/30\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.6938 - acc: 0.5142\n",
      "666/666 [==============================] - 0s 229us/step - loss: 0.6928 - acc: 0.5150\n",
      "Epoch 20/60\n",
      "667/667 [==============================] - 0s 98us/step - loss: 0.6930 - acc: 0.5352\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6904 - acc: 0.5312Epoch 21/30\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6925 - acc: 0.5135\n",
      "Epoch 21/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6928 - acc: 0.5187\n",
      "Epoch 22/30\n",
      "666/666 [==============================] - 0s 37us/step - loss: 0.6924 - acc: 0.5195\n",
      "Epoch 22/60\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6928 - acc: 0.5157\n",
      "Epoch 23/30\n",
      "667/667 [==============================] - 0s 171us/step - loss: 0.6931 - acc: 0.5082\n",
      "Epoch 18/30\n",
      "666/666 [==============================] - 0s 40us/step - loss: 0.6922 - acc: 0.5165\n",
      "Epoch 23/60\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6928 - acc: 0.5127\n",
      "Epoch 24/30\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6931 - acc: 0.5312Epoch 7/60\n",
      "667/667 [==============================] - 0s 49us/step - loss: 0.6932 - acc: 0.5172\n",
      "Epoch 19/30\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6936 - acc: 0.5187\n",
      "Epoch 8/60\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.6929 - acc: 0.5157\n",
      "Epoch 20/30\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6932 - acc: 0.5127\n",
      "Epoch 9/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6925 - acc: 0.5142\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6930 - acc: 0.5142\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "666/666 [==============================] - 0s 136us/step - loss: 0.6920 - acc: 0.5225\n",
      "Epoch 24/60\n",
      "Epoch 10/60\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6905 - acc: 0.5000Epoch 21/30\n",
      "666/666 [==============================] - 0s 43us/step - loss: 0.6919 - acc: 0.5255\n",
      "Epoch 25/60\n",
      "667/667 [==============================] - 0s 61us/step - loss: 0.6927 - acc: 0.5172\n",
      "Epoch 11/60\n",
      "667/667 [==============================] - 0s 230us/step - loss: 0.6929 - acc: 0.5232\n",
      "Epoch 25/30\n",
      "667/667 [==============================] - 0s 70us/step - loss: 0.6928 - acc: 0.5172\n",
      "667/667 [==============================] - 0s 46us/step - loss: 0.6926 - acc: 0.5172\n",
      "Epoch 12/60\n",
      "Epoch 26/30\n",
      "667/667 [==============================] - 0s 46us/step - loss: 0.6928 - acc: 0.5127\n",
      "666/666 [==============================] - 0s 137us/step - loss: 0.6917 - acc: 0.5345\n",
      "Epoch 27/30\n",
      "Epoch 26/60\n",
      "667/667 [==============================] - 0s 78us/step - loss: 0.6924 - acc: 0.5157\n",
      "Epoch 13/60\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6924 - acc: 0.5157\n",
      "667/667 [==============================] - 0s 256us/step - loss: 0.6924 - acc: 0.5172\n",
      "Epoch 22/30\n",
      "666/666 [==============================] - 0s 73us/step - loss: 0.6916 - acc: 0.5300\n",
      "Epoch 27/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6922 - acc: 0.5292\n",
      "Epoch 23/30\n",
      "666/666 [==============================] - 0s 45us/step - loss: 0.6915 - acc: 0.5435\n",
      "Epoch 28/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6921 - acc: 0.5217\n",
      "Epoch 24/30\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6970 - acc: 0.5312Epoch 28/30\n",
      "666/666 [==============================] - 0s 49us/step - loss: 0.6913 - acc: 0.5420\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6876 - acc: 0.5469Epoch 29/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6920 - acc: 0.5307\n",
      "Epoch 25/30\n",
      "667/667 [==============================] - 0s 67us/step - loss: 0.6924 - acc: 0.5187\n",
      "Epoch 29/30\n",
      "667/667 [==============================] - 0s 232us/step - loss: 0.6923 - acc: 0.5172\n",
      "Epoch 14/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6924 - acc: 0.5247\n",
      "Epoch 30/30\n",
      "667/667 [==============================] - 0s 114us/step - loss: 0.6920 - acc: 0.5277\n",
      "667/667 [==============================] - 0s 61us/step - loss: 0.6922 - acc: 0.5112\n",
      "Epoch 26/30\n",
      "Epoch 15/60\n",
      "667/667 [==============================] - 0s 36us/step - loss: 0.6920 - acc: 0.5262\n",
      "Epoch 27/30\n",
      "667/667 [==============================] - 0s 70us/step - loss: 0.6923 - acc: 0.5232\n",
      "667/667 [==============================] - 0s 44us/step - loss: 0.6922 - acc: 0.5127\n",
      "Epoch 16/60\n",
      "666/666 [==============================] - 0s 239us/step - loss: 0.6915 - acc: 0.5375\n",
      "Epoch 30/60\n",
      "666/666 [==============================] - 0s 38us/step - loss: 0.6910 - acc: 0.5495\n",
      "Epoch 31/60\n",
      "667/667 [==============================] - 0s 70us/step - loss: 0.6921 - acc: 0.5127\n",
      "Epoch 17/60\n",
      "666/666 [==============================] - 0s 41us/step - loss: 0.6909 - acc: 0.5360\n",
      "Epoch 32/60\n",
      "667/667 [==============================] - 0s 180us/step - loss: 0.6916 - acc: 0.5292\n",
      "666/666 [==============================] - 0s 36us/step - loss: 0.6908 - acc: 0.5405\n",
      "Epoch 33/60\n",
      "Epoch 28/30\n",
      "667/667 [==============================] - 0s 62us/step - loss: 0.6918 - acc: 0.5202\n",
      "Epoch 18/60\n",
      "667/667 [==============================] - 0s 50us/step - loss: 0.6917 - acc: 0.5232\n",
      "Epoch 29/30\n",
      "667/667 [==============================] - 0s 61us/step - loss: 0.6918 - acc: 0.5262\n",
      "Epoch 19/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6916 - acc: 0.5262\n",
      "Epoch 30/30\n",
      "667/667 [==============================] - 0s 48us/step - loss: 0.6917 - acc: 0.5277\n",
      "Epoch 20/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6918 - acc: 0.5277\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6916 - acc: 0.5202\n",
      "666/666 [==============================] - 0s 194us/step - loss: 0.6906 - acc: 0.5390\n",
      "Epoch 21/60\n",
      "Epoch 34/60\n",
      "333/333 [==============================] - 0s 834us/steps: 0.6944 - acc: 0.40\n",
      "666/666 [==============================] - 0s 47us/step - loss: 0.6906 - acc: 0.5435\n",
      "Epoch 35/60\n",
      "667/667 [==============================] - 0s 24us/stepss: 0.6853 - acc: 0.62\n",
      "666/666 [==============================] - 0s 43us/step - loss: 0.6904 - acc: 0.5511\n",
      "Epoch 36/60\n",
      "666/666 [==============================] - 0s 42us/step - loss: 0.6902 - acc: 0.5480\n",
      "Epoch 37/60\n",
      "667/667 [==============================] - 0s 187us/step - loss: 0.6913 - acc: 0.5247\n",
      "Epoch 22/60\n",
      "666/666 [==============================] - 0s 37us/step - loss: 0.6901 - acc: 0.5511\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6860 - acc: 0.5625Epoch 38/60\n",
      "667/667 [==============================] - 0s 44us/step - loss: 0.6913 - acc: 0.5217\n",
      "Epoch 23/60\n",
      "333/333 [==============================] - 0s 586us/steps: 0.6940 - acc: 0.46\n",
      "667/667 [==============================] - 0s 20us/step\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6914 - acc: 0.5172\n",
      "Epoch 24/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6910 - acc: 0.5202\n",
      "Epoch 25/60\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6910 - acc: 0.5247\n",
      "Epoch 26/60\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6730 - acc: 0.6562"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jmoolay/anaconda3/lib/python3.6/site-packages/keras/callbacks.py:120: UserWarning: Method on_batch_end() is slow compared to the batch update (0.125650). Check your callbacks.\n",
      "  % delta_t_median)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "666/666 [==============================] - 0s 242us/step - loss: 0.6899 - acc: 0.5511\n",
      "Epoch 39/60\n",
      "666/666 [==============================] - 0s 38us/step - loss: 0.6898 - acc: 0.5616\n",
      "Epoch 40/60\n",
      "666/666 [==============================] - 0s 37us/step - loss: 0.6897 - acc: 0.5480\n",
      "Epoch 41/60\n",
      "666/666 [==============================] - 0s 38us/step - loss: 0.6895 - acc: 0.5661\n",
      "667/667 [==============================] - 0s 189us/step - loss: 0.6909 - acc: 0.5232\n",
      "Epoch 27/60\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6760 - acc: 0.6250Epoch 42/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6909 - acc: 0.5262\n",
      "Epoch 28/60\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6908 - acc: 0.5217\n",
      "Epoch 29/60\n",
      "666/666 [==============================] - 0s 49us/step - loss: 0.6893 - acc: 0.5631\n",
      "Epoch 43/60\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6908 - acc: 0.5247\n",
      "666/666 [==============================] - 0s 37us/step - loss: 0.6892 - acc: 0.5646\n",
      "Epoch 44/60\n",
      "Epoch 30/60\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6890 - acc: 0.5826\n",
      "Epoch 45/60\n",
      "667/667 [==============================] - 0s 72us/step - loss: 0.6908 - acc: 0.5187\n",
      "Epoch 31/60\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6887 - acc: 0.5751\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6850 - acc: 0.5000Epoch 46/60\n",
      "666/666 [==============================] - 0s 43us/step - loss: 0.6887 - acc: 0.5796\n",
      "Epoch 47/60\n",
      "667/667 [==============================] - 0s 64us/step - loss: 0.6906 - acc: 0.5202\n",
      "Epoch 32/60\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6885 - acc: 0.5781\n",
      "Epoch 48/60\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6906 - acc: 0.5292\n",
      "Epoch 33/60\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6882 - acc: 0.5706\n",
      "Epoch 49/60\n",
      "667/667 [==============================] - 0s 47us/step - loss: 0.6906 - acc: 0.5262\n",
      "Epoch 34/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6905 - acc: 0.5247\n",
      "Epoch 35/60\n",
      "666/666 [==============================] - 0s 51us/step - loss: 0.6882 - acc: 0.5736\n",
      "Epoch 50/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6905 - acc: 0.5247\n",
      "Epoch 36/60\n",
      "666/666 [==============================] - 0s 41us/step - loss: 0.6879 - acc: 0.5736\n",
      "Epoch 51/60\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6903 - acc: 0.5262\n",
      "Epoch 37/60\n",
      "666/666 [==============================] - 0s 85us/step - loss: 0.6878 - acc: 0.5721\n",
      "Epoch 52/60\n",
      "666/666 [==============================] - 0s 45us/step - loss: 0.6877 - acc: 0.5796\n",
      "Epoch 53/60\n",
      "667/667 [==============================] - 0s 144us/step - loss: 0.6904 - acc: 0.5247\n",
      "Epoch 38/60\n",
      "666/666 [==============================] - 0s 46us/step - loss: 0.6876 - acc: 0.5766\n",
      "Epoch 54/60\n",
      "667/667 [==============================] - 0s 51us/step - loss: 0.6903 - acc: 0.5187\n",
      "Epoch 39/60\n",
      "667/667 [==============================] - 0s 44us/step - loss: 0.6902 - acc: 0.5217\n",
      "Epoch 40/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6902 - acc: 0.5172\n",
      "Epoch 41/60\n",
      "666/666 [==============================] - 0s 152us/step - loss: 0.6874 - acc: 0.5796\n",
      "Epoch 55/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6900 - acc: 0.5187\n",
      "Epoch 42/60\n",
      "666/666 [==============================] - 0s 43us/step - loss: 0.6873 - acc: 0.5811\n",
      "Epoch 56/60\n",
      "666/666 [==============================] - 0s 48us/step - loss: 0.6872 - acc: 0.5781\n",
      "Epoch 57/60\n",
      "666/666 [==============================] - 0s 41us/step - loss: 0.6872 - acc: 0.5811\n",
      "667/667 [==============================] - 0s 124us/step - loss: 0.6902 - acc: 0.5337\n",
      "Epoch 58/60\n",
      "Epoch 43/60\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6870 - acc: 0.5886\n",
      "Epoch 59/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6901 - acc: 0.5292\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6861 - acc: 0.5312Epoch 44/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6899 - acc: 0.5202\n",
      "Epoch 45/60\n",
      "666/666 [==============================] - 0s 78us/step - loss: 0.6869 - acc: 0.5856\n",
      "Epoch 60/60\n",
      "667/667 [==============================] - 0s 54us/step - loss: 0.6899 - acc: 0.5187\n",
      " 32/666 [>.............................] - ETA: 0s - loss: 0.6805 - acc: 0.6875Epoch 46/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6898 - acc: 0.5217\n",
      "Epoch 47/60\n",
      "666/666 [==============================] - 0s 104us/step - loss: 0.6869 - acc: 0.5796\n",
      "667/667 [==============================] - 0s 56us/step - loss: 0.6898 - acc: 0.5247\n",
      "Epoch 48/60\n",
      "667/667 [==============================] - 0s 47us/step - loss: 0.6898 - acc: 0.5217\n",
      "Epoch 49/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6899 - acc: 0.5337\n",
      "Epoch 50/60\n",
      "334/334 [==============================] - 0s 349us/steps: 0.6806 - acc: 0.59\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6897 - acc: 0.5232\n",
      "Epoch 51/60\n",
      "666/666 [==============================] - 0s 36us/stepss: 0.6990 - acc: 0.43\n",
      "667/667 [==============================] - 0s 47us/step - loss: 0.6897 - acc: 0.5142\n",
      "Epoch 52/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6895 - acc: 0.5217\n",
      "Epoch 53/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6896 - acc: 0.5202\n",
      "Epoch 54/60\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.6896 - acc: 0.5142\n",
      "Epoch 55/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6894 - acc: 0.5262\n",
      "Epoch 56/60\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.6894 - acc: 0.5157\n",
      "Epoch 57/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6893 - acc: 0.5202\n",
      "Epoch 58/60\n",
      "667/667 [==============================] - 0s 56us/step - loss: 0.6894 - acc: 0.5232\n",
      "Epoch 59/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6894 - acc: 0.5142\n",
      "Epoch 60/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6893 - acc: 0.5217\n",
      "Epoch 1/15\n",
      "Epoch 1/60\n",
      "333/333 [==============================] - 0s 280us/step\n",
      "667/667 [==============================] - 0s 22us/step\n",
      "666/666 [==============================] - 1s 751us/step - loss: 0.6956 - acc: 0.5045\n",
      "Epoch 2/15\n",
      "667/667 [==============================] - 0s 743us/step - loss: 0.6971 - acc: 0.4783\n",
      " 64/666 [=>............................] - ETA: 0s - loss: 0.6900 - acc: 0.5312Epoch 2/60\n",
      "666/666 [==============================] - 0s 33us/step - loss: 0.6949 - acc: 0.5060\n",
      "Epoch 3/15\n",
      "666/666 [==============================] - 0s 25us/step - loss: 0.6946 - acc: 0.4985\n",
      "Epoch 4/15\n",
      "667/667 [==============================] - 0s 67us/step - loss: 0.6951 - acc: 0.4738\n",
      "Epoch 3/60\n",
      "Epoch 1/15\n",
      "666/666 [==============================] - 0s 71us/step - loss: 0.6941 - acc: 0.5000\n",
      "Epoch 5/15\n",
      "666/666 [==============================] - 0s 29us/step - loss: 0.6937 - acc: 0.4970\n",
      "Epoch 6/15\n",
      "667/667 [==============================] - 0s 47us/step - loss: 0.6943 - acc: 0.4933\n",
      "Epoch 4/60\n",
      "666/666 [==============================] - 0s 31us/step - loss: 0.6934 - acc: 0.4955\n",
      "Epoch 7/15\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6932 - acc: 0.5000\n",
      "Epoch 8/15\n",
      "667/667 [==============================] - 0s 50us/step - loss: 0.6936 - acc: 0.5037\n",
      "Epoch 5/60\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.6932 - acc: 0.5217\n",
      "Epoch 6/60\n",
      "666/666 [==============================] - 0s 59us/step - loss: 0.6929 - acc: 0.5000\n",
      "Epoch 9/15\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6927 - acc: 0.5165\n",
      "Epoch 10/15\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6930 - acc: 0.5187\n",
      "Epoch 7/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6924 - acc: 0.5315\n",
      "Epoch 11/15\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6928 - acc: 0.5022\n",
      "Epoch 8/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6922 - acc: 0.5315\n",
      "Epoch 12/15\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6921 - acc: 0.5300\n",
      "Epoch 13/15\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6925 - acc: 0.5112\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6919 - acc: 0.5345\n",
      "Epoch 14/15\n",
      " 64/666 [=>............................] - ETA: 0s - loss: 0.6926 - acc: 0.5469Epoch 9/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6917 - acc: 0.5405\n",
      "Epoch 15/15\n",
      "666/666 [==============================] - 0s 26us/step - loss: 0.6915 - acc: 0.5315\n",
      "667/667 [==============================] - 0s 59us/step - loss: 0.6925 - acc: 0.5037\n",
      "Epoch 10/60\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6923 - acc: 0.5112\n",
      "Epoch 11/60\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6920 - acc: 0.5097\n",
      "Epoch 12/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6920 - acc: 0.5127\n",
      "Epoch 13/60\n",
      " 32/667 [>.............................] - ETA: 0s - loss: 0.6912 - acc: 0.5000Epoch 1/15\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6917 - acc: 0.5187\n",
      "Epoch 14/60\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6916 - acc: 0.5157\n",
      "Epoch 15/60\n",
      "334/334 [==============================] - 0s 523us/steps: 0.6908 - acc: 0.50\n",
      "666/666 [==============================] - 0s 17us/step\n",
      "667/667 [==============================] - 0s 53us/step - loss: 0.6916 - acc: 0.5112\n",
      "Epoch 16/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6913 - acc: 0.5277\n",
      "Epoch 17/60\n",
      "667/667 [==============================] - 0s 44us/step - loss: 0.6912 - acc: 0.5202\n",
      "Epoch 18/60\n",
      "667/667 [==============================] - 0s 48us/step - loss: 0.6911 - acc: 0.5277\n",
      "Epoch 19/60\n",
      "667/667 [==============================] - 0s 64us/step - loss: 0.6909 - acc: 0.5337\n",
      "Epoch 20/60\n",
      "667/667 [==============================] - 0s 50us/step - loss: 0.6908 - acc: 0.5262\n",
      "Epoch 21/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6907 - acc: 0.5232\n",
      "Epoch 22/60\n",
      "667/667 [==============================] - 0s 56us/step - loss: 0.6906 - acc: 0.5247\n",
      "Epoch 23/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6905 - acc: 0.5337\n",
      "Epoch 24/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6904 - acc: 0.5232\n",
      "Epoch 25/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6904 - acc: 0.5247\n",
      "Epoch 26/60\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6902 - acc: 0.5322\n",
      "Epoch 27/60\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.6957 - acc: 0.5172\n",
      "Epoch 2/15\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6901 - acc: 0.5352\n",
      "Epoch 28/60\n",
      "667/667 [==============================] - 0s 32us/step - loss: 0.6952 - acc: 0.5262\n",
      "Epoch 3/15\n",
      "667/667 [==============================] - 0s 28us/step - loss: 0.6951 - acc: 0.5307\n",
      "667/667 [==============================] - 0s 47us/step - loss: 0.6901 - acc: 0.5232\n",
      "Epoch 29/60\n",
      "Epoch 4/15\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6948 - acc: 0.5277\n",
      "Epoch 5/15\n",
      "667/667 [==============================] - 0s 55us/step - loss: 0.6901 - acc: 0.5367\n",
      "Epoch 30/60\n",
      "667/667 [==============================] - 0s 32us/step - loss: 0.6947 - acc: 0.5247\n",
      "Epoch 6/15\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6946 - acc: 0.5217\n",
      "Epoch 7/15\n",
      "667/667 [==============================] - 0s 49us/step - loss: 0.6899 - acc: 0.5367\n",
      "Epoch 31/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6943 - acc: 0.5202\n",
      "Epoch 8/15\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6942 - acc: 0.5172\n",
      "Epoch 9/15\n",
      "667/667 [==============================] - 0s 50us/step - loss: 0.6898 - acc: 0.5337\n",
      "Epoch 32/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6941 - acc: 0.5157\n",
      "Epoch 10/15\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6939 - acc: 0.5157\n",
      "Epoch 11/15\n",
      "667/667 [==============================] - 0s 51us/step - loss: 0.6897 - acc: 0.5397\n",
      "Epoch 33/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6897 - acc: 0.5382\n",
      "Epoch 34/60\n",
      "667/667 [==============================] - 0s 76us/step - loss: 0.6938 - acc: 0.5187\n",
      "Epoch 12/15\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6937 - acc: 0.5172\n",
      "667/667 [==============================] - 0s 43us/step - loss: 0.6896 - acc: 0.5262\n",
      "Epoch 13/15\n",
      "Epoch 35/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6936 - acc: 0.5187\n",
      "Epoch 14/15\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6898 - acc: 0.5307\n",
      "Epoch 36/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6935 - acc: 0.5142\n",
      "Epoch 15/15\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6934 - acc: 0.5142\n",
      "667/667 [==============================] - 0s 64us/step - loss: 0.6894 - acc: 0.5307\n",
      "Epoch 37/60\n",
      "667/667 [==============================] - 0s 44us/step - loss: 0.6894 - acc: 0.5337\n",
      "Epoch 38/60\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6894 - acc: 0.5277\n",
      "Epoch 39/60\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.7229 - acc: 0.4783\n",
      "Epoch 2/15\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.6893 - acc: 0.5352\n",
      "Epoch 40/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.7159 - acc: 0.4663\n",
      "667/667 [==============================] - 0s 90us/step - loss: 0.6892 - acc: 0.5337\n",
      "Epoch 41/60\n",
      "Epoch 3/15\n",
      "667/667 [==============================] - 0s 41us/step - loss: 0.7105 - acc: 0.4783\n",
      "667/667 [==============================] - 0s 50us/step - loss: 0.6893 - acc: 0.5352\n",
      "Epoch 4/15\n",
      "Epoch 42/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.7065 - acc: 0.4753\n",
      "Epoch 5/15\n",
      "333/333 [==============================] - 0s 737us/steps: 0.6961 - acc: 0.51\n",
      "667/667 [==============================] - 0s 11us/step\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.7039 - acc: 0.4738\n",
      "Epoch 6/15\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.7020 - acc: 0.4663\n",
      "Epoch 7/15\n",
      "667/667 [==============================] - 0s 102us/step - loss: 0.6892 - acc: 0.5322\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.7006 - acc: 0.4693\n",
      "Epoch 8/15\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6996 - acc: 0.4873\n",
      "Epoch 9/15\n",
      "Epoch 43/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6989 - acc: 0.4978\n",
      "Epoch 10/15\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6980 - acc: 0.5067\n",
      "Epoch 11/15\n",
      "667/667 [==============================] - 0s 61us/step - loss: 0.6890 - acc: 0.5292\n",
      "Epoch 44/60\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6973 - acc: 0.5052\n",
      "Epoch 1/30\n",
      "Epoch 12/15\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6890 - acc: 0.5322\n",
      "Epoch 45/60\n",
      "667/667 [==============================] - 0s 30us/step - loss: 0.6967 - acc: 0.5142\n",
      "Epoch 13/15\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6889 - acc: 0.5337\n",
      "Epoch 46/60\n",
      "667/667 [==============================] - 0s 36us/step - loss: 0.6962 - acc: 0.5262\n",
      "Epoch 14/15\n",
      "667/667 [==============================] - 0s 55us/step - loss: 0.6889 - acc: 0.5397\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6957 - acc: 0.5277\n",
      "Epoch 15/15\n",
      "Epoch 47/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6952 - acc: 0.5277\n",
      "667/667 [==============================] - 0s 85us/step - loss: 0.6888 - acc: 0.5307\n",
      "Epoch 48/60\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6889 - acc: 0.5367\n",
      "Epoch 49/60\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6887 - acc: 0.5367\n",
      "Epoch 50/60\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6888 - acc: 0.5292\n",
      "Epoch 51/60\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6886 - acc: 0.5277\n",
      "Epoch 52/60\n",
      "333/333 [==============================] - 0s 525us/steps: 0.6840 - acc: 0.59\n",
      "667/667 [==============================] - 0s 12us/step\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6886 - acc: 0.5442\n",
      "Epoch 53/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6885 - acc: 0.5352\n",
      "Epoch 54/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "667/667 [==============================] - 0s 39us/step - loss: 0.6885 - acc: 0.5397\n",
      "Epoch 55/60\n",
      "667/667 [==============================] - 0s 48us/step - loss: 0.6885 - acc: 0.5322\n",
      "Epoch 56/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6884 - acc: 0.5322\n",
      "Epoch 57/60\n",
      "667/667 [==============================] - 0s 39us/step - loss: 0.6883 - acc: 0.5352\n",
      "Epoch 58/60\n",
      "667/667 [==============================] - 0s 51us/step - loss: 0.6885 - acc: 0.5337\n",
      "Epoch 59/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6883 - acc: 0.5307\n",
      "Epoch 60/60\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.6885 - acc: 0.5262\n",
      "333/333 [==============================] - 0s 241us/step\n",
      "667/667 [==============================] - 0s 21us/step\n",
      "Epoch 1/30\n",
      "666/666 [==============================] - 1s 1ms/step - loss: 0.7021 - acc: 0.4970\n",
      "Epoch 2/30\n",
      "666/666 [==============================] - 0s 34us/step - loss: 0.6990 - acc: 0.4985\n",
      "Epoch 3/30\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6971 - acc: 0.4940\n",
      "Epoch 4/30\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6954 - acc: 0.4910\n",
      "Epoch 5/30\n",
      "666/666 [==============================] - 0s 19us/step - loss: 0.6941 - acc: 0.4970\n",
      "Epoch 6/30\n",
      "666/666 [==============================] - 0s 20us/step - loss: 0.6930 - acc: 0.5015\n",
      "Epoch 7/30\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6925 - acc: 0.5090\n",
      "Epoch 8/30\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6917 - acc: 0.5285\n",
      "Epoch 9/30\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6913 - acc: 0.5435\n",
      "Epoch 10/30\n",
      " 64/666 [=>............................] - ETA: 0s - loss: 0.6879 - acc: 0.5312Epoch 1/30\n",
      "666/666 [==============================] - 0s 44us/step - loss: 0.6909 - acc: 0.5511\n",
      "Epoch 11/30\n",
      "666/666 [==============================] - 0s 26us/step - loss: 0.6906 - acc: 0.5360\n",
      "Epoch 12/30\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6902 - acc: 0.5435\n",
      "Epoch 13/30\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6899 - acc: 0.5526\n",
      "Epoch 14/30\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6896 - acc: 0.5571\n",
      "Epoch 15/30\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6894 - acc: 0.5601\n",
      "Epoch 16/30\n",
      "666/666 [==============================] - 0s 28us/step - loss: 0.6892 - acc: 0.5661\n",
      "Epoch 17/30\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6891 - acc: 0.5631\n",
      "Epoch 18/30\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6889 - acc: 0.5706\n",
      "Epoch 19/30\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6887 - acc: 0.5661\n",
      "Epoch 20/30\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6887 - acc: 0.5631\n",
      "Epoch 21/30\n",
      "666/666 [==============================] - 0s 28us/step - loss: 0.6885 - acc: 0.5631\n",
      "Epoch 22/30\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6884 - acc: 0.5541\n",
      "Epoch 23/30\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6883 - acc: 0.5661\n",
      "Epoch 24/30\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6882 - acc: 0.5646\n",
      "Epoch 25/30\n",
      "666/666 [==============================] - 0s 20us/step - loss: 0.6881 - acc: 0.5676\n",
      "Epoch 26/30\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6879 - acc: 0.5646\n",
      "Epoch 27/30\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6879 - acc: 0.5586\n",
      "Epoch 28/30\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6877 - acc: 0.5541\n",
      "Epoch 29/30\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6876 - acc: 0.5541\n",
      "Epoch 30/30\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6876 - acc: 0.5571\n",
      "Epoch 1/60\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.7190 - acc: 0.4903\n",
      "Epoch 2/30\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.7124 - acc: 0.4933\n",
      "Epoch 3/30\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.7066 - acc: 0.4933\n",
      "Epoch 4/30\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.7028 - acc: 0.4963\n",
      "Epoch 5/30\n",
      "334/334 [==============================] - 0s 472us/step\n",
      "666/666 [==============================] - 0s 15us/stepss: 0.6975 - acc: 0.54\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.7007 - acc: 0.4963\n",
      "Epoch 6/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6986 - acc: 0.4918\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6973 - acc: 0.4903\n",
      "Epoch 8/30\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6964 - acc: 0.5022\n",
      "Epoch 9/30\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6957 - acc: 0.5037\n",
      "Epoch 10/30\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6953 - acc: 0.5127\n",
      "Epoch 11/30\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6949 - acc: 0.5202\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6945 - acc: 0.5217\n",
      "Epoch 13/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6942 - acc: 0.5337\n",
      "Epoch 14/30\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6940 - acc: 0.5292\n",
      "Epoch 15/30\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6938 - acc: 0.5427\n",
      "Epoch 16/30\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6935 - acc: 0.5382\n",
      "Epoch 17/30\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6935 - acc: 0.5322\n",
      "Epoch 18/30\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.7044 - acc: 0.5262\n",
      "Epoch 2/30\n",
      "667/667 [==============================] - 0s 31us/step - loss: 0.6932 - acc: 0.5247\n",
      "Epoch 19/30\n",
      "667/667 [==============================] - 0s 28us/step - loss: 0.6930 - acc: 0.5262\n",
      "Epoch 20/30\n",
      "667/667 [==============================] - 0s 40us/step - loss: 0.7009 - acc: 0.5232\n",
      "Epoch 3/30\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6983 - acc: 0.5232\n",
      "Epoch 4/30\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6929 - acc: 0.5307\n",
      "Epoch 21/30\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6969 - acc: 0.5142\n",
      "Epoch 5/30\n",
      "667/667 [==============================] - 0s 28us/step - loss: 0.6928 - acc: 0.5337\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.7097 - acc: 0.4219Epoch 22/30\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6963 - acc: 0.5127\n",
      "Epoch 6/30\n",
      "667/667 [==============================] - 0s 29us/step - loss: 0.6927 - acc: 0.5292\n",
      "Epoch 23/30\n",
      "667/667 [==============================] - 0s 32us/step - loss: 0.6956 - acc: 0.5112\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 0s 30us/step - loss: 0.6927 - acc: 0.5202\n",
      "Epoch 24/30\n",
      "667/667 [==============================] - 0s 35us/step - loss: 0.6953 - acc: 0.5097\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6926 - acc: 0.5187\n",
      "Epoch 8/30\n",
      "Epoch 25/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6949 - acc: 0.4993\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6922 - acc: 0.5247\n",
      "Epoch 9/30\n",
      "Epoch 26/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6949 - acc: 0.4918\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6921 - acc: 0.5157\n",
      "Epoch 10/30\n",
      "Epoch 27/30\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6946 - acc: 0.4978\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6920 - acc: 0.5067\n",
      "Epoch 11/30\n",
      "Epoch 28/30\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6944 - acc: 0.4903\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6942 - acc: 0.4933\n",
      "Epoch 13/30\n",
      "667/667 [==============================] - 0s 71us/step - loss: 0.6920 - acc: 0.5067\n",
      "Epoch 29/30\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6939 - acc: 0.5067\n",
      "Epoch 14/30\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6920 - acc: 0.5127\n",
      "Epoch 30/30\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6937 - acc: 0.5022\n",
      "Epoch 15/30\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6918 - acc: 0.5082\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6936 - acc: 0.5052\n",
      "Epoch 16/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6934 - acc: 0.5142\n",
      "Epoch 17/30\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6932 - acc: 0.5112\n",
      "Epoch 18/30\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6929 - acc: 0.5172\n",
      "Epoch 19/30\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6928 - acc: 0.5157\n",
      "Epoch 20/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6928 - acc: 0.5127\n",
      "Epoch 21/30\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6924 - acc: 0.5127\n",
      "Epoch 22/30\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6923 - acc: 0.5082\n",
      "Epoch 23/30\n",
      "666/666 [==============================] - 1s 1ms/step - loss: 0.7111 - acc: 0.5135\n",
      "Epoch 2/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6921 - acc: 0.5112\n",
      "666/666 [==============================] - 0s 28us/step - loss: 0.7060 - acc: 0.5135\n",
      " 64/333 [====>.........................] - ETA: 0sEpoch 3/60\n",
      "333/333 [==============================] - 0s 606us/step\n",
      "667/667 [==============================] - 0s 14us/stepss: 0.7110 - acc: 0.46\n",
      "Epoch 24/30\n",
      "666/666 [==============================] - 0s 30us/step - loss: 0.7027 - acc: 0.5060\n",
      "Epoch 4/60\n",
      "667/667 [==============================] - 0s 36us/step - loss: 0.6919 - acc: 0.5262\n",
      "666/666 [==============================] - 0s 20us/step - loss: 0.7002 - acc: 0.4910\n",
      "Epoch 5/60\n",
      "Epoch 25/30\n",
      "666/666 [==============================] - 0s 20us/step - loss: 0.6989 - acc: 0.4715\n",
      "Epoch 6/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6918 - acc: 0.5277\n",
      " 64/666 [=>............................] - ETA: 0s - loss: 0.6864 - acc: 0.5938Epoch 26/30\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6977 - acc: 0.4580\n",
      "Epoch 7/60\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6918 - acc: 0.5262\n",
      "Epoch 27/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6915 - acc: 0.5277\n",
      "Epoch 28/30\n",
      "666/666 [==============================] - 0s 53us/step - loss: 0.6972 - acc: 0.4550\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6899 - acc: 0.5625Epoch 8/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6914 - acc: 0.5277\n",
      "Epoch 29/30\n",
      "666/666 [==============================] - 0s 27us/step - loss: 0.6966 - acc: 0.4550\n",
      "Epoch 9/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6912 - acc: 0.5277\n",
      "Epoch 30/30\n",
      "666/666 [==============================] - 0s 29us/step - loss: 0.6961 - acc: 0.4595\n",
      "Epoch 10/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6911 - acc: 0.5262\n",
      "666/666 [==============================] - 0s 29us/step - loss: 0.6956 - acc: 0.4640\n",
      "Epoch 11/60\n",
      "666/666 [==============================] - 0s 30us/step - loss: 0.6952 - acc: 0.4640\n",
      "Epoch 12/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6948 - acc: 0.4685\n",
      "Epoch 13/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6945 - acc: 0.4685\n",
      "Epoch 14/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6941 - acc: 0.4805\n",
      "Epoch 15/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6938 - acc: 0.4760\n",
      "Epoch 16/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6934 - acc: 0.4790\n",
      "Epoch 17/60\n",
      "666/666 [==============================] - 0s 25us/step - loss: 0.6931 - acc: 0.4880\n",
      "Epoch 18/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6926 - acc: 0.4865\n",
      "Epoch 19/60\n",
      "333/333 [==============================] - 0s 585us/steps: 0.6897 - acc: 0.50\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6926 - acc: 0.4895\n",
      "Epoch 20/60\n",
      "667/667 [==============================] - 0s 12us/stepss: 0.6928 - acc: 0.51\n",
      "666/666 [==============================] - 0s 29us/step - loss: 0.6919 - acc: 0.5015\n",
      "Epoch 21/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6917 - acc: 0.5180\n",
      "Epoch 22/60\n",
      " 64/666 [=>............................] - ETA: 0s - loss: 0.6916 - acc: 0.5625Epoch 1/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6914 - acc: 0.5255\n",
      "Epoch 23/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6912 - acc: 0.5315\n",
      "Epoch 24/60\n",
      "666/666 [==============================] - 0s 25us/step - loss: 0.6908 - acc: 0.5450\n",
      "Epoch 25/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6905 - acc: 0.5450\n",
      "Epoch 26/60\n",
      "666/666 [==============================] - 0s 25us/step - loss: 0.6903 - acc: 0.5405\n",
      "Epoch 27/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6901 - acc: 0.5435\n",
      "Epoch 28/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6898 - acc: 0.5450\n",
      "Epoch 29/60\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6898 - acc: 0.5390\n",
      "Epoch 30/60\n",
      "666/666 [==============================] - 0s 27us/step - loss: 0.6894 - acc: 0.5465\n",
      "Epoch 31/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6892 - acc: 0.5526\n",
      "Epoch 32/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6888 - acc: 0.5495\n",
      "Epoch 33/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6886 - acc: 0.5480\n",
      "Epoch 34/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6885 - acc: 0.5541\n",
      "Epoch 35/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6883 - acc: 0.5511\n",
      "Epoch 36/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6881 - acc: 0.5586\n",
      "Epoch 37/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6879 - acc: 0.5646\n",
      "Epoch 38/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6878 - acc: 0.5631\n",
      "Epoch 39/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6876 - acc: 0.5676\n",
      "Epoch 40/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6875 - acc: 0.5646\n",
      "Epoch 41/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6875 - acc: 0.5646\n",
      "Epoch 42/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6872 - acc: 0.5646\n",
      "Epoch 43/60\n",
      "666/666 [==============================] - 0s 30us/step - loss: 0.6872 - acc: 0.5616\n",
      "Epoch 44/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6870 - acc: 0.5616\n",
      "Epoch 45/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6871 - acc: 0.5631\n",
      "Epoch 46/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6868 - acc: 0.5706\n",
      "Epoch 47/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6867 - acc: 0.5661\n",
      "Epoch 48/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6865 - acc: 0.5631\n",
      "Epoch 49/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6868 - acc: 0.5616\n",
      "Epoch 50/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6863 - acc: 0.5661\n",
      "Epoch 51/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6862 - acc: 0.5721\n",
      "Epoch 52/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6861 - acc: 0.5706\n",
      "Epoch 53/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6861 - acc: 0.5736\n",
      "Epoch 54/60\n",
      " 64/666 [=>............................] - ETA: 0s - loss: 0.6736 - acc: 0.6094Epoch 1/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6862 - acc: 0.5736\n",
      "Epoch 55/60\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6860 - acc: 0.5721\n",
      "Epoch 56/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6860 - acc: 0.5721\n",
      "Epoch 57/60\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6859 - acc: 0.5736\n",
      "Epoch 58/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6857 - acc: 0.5751\n",
      "Epoch 59/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6856 - acc: 0.5736\n",
      "Epoch 60/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6855 - acc: 0.5766\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.7412 - acc: 0.5247\n",
      "Epoch 2/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "667/667 [==============================] - 0s 19us/step - loss: 0.7286 - acc: 0.5202\n",
      "Epoch 3/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.7197 - acc: 0.5187\n",
      "Epoch 4/60\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6841 - acc: 0.6250Epoch 1/15\n",
      "667/667 [==============================] - 0s 35us/step - loss: 0.7124 - acc: 0.5172\n",
      "Epoch 5/60\n",
      "334/334 [==============================] - 0s 443us/step\n",
      "666/666 [==============================] - 0s 14us/stepss: 0.7164 - acc: 0.45\n",
      "667/667 [==============================] - 0s 32us/step - loss: 0.7084 - acc: 0.5112\n",
      "Epoch 6/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.7055 - acc: 0.4948\n",
      "Epoch 7/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.7032 - acc: 0.4903\n",
      "Epoch 8/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.7019 - acc: 0.5007\n",
      "Epoch 9/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.7011 - acc: 0.4993\n",
      "Epoch 10/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.7003 - acc: 0.4993\n",
      "Epoch 11/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6999 - acc: 0.5022\n",
      "Epoch 12/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6995 - acc: 0.5127\n",
      "Epoch 13/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6991 - acc: 0.5097\n",
      "Epoch 14/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6989 - acc: 0.5052\n",
      "Epoch 15/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6986 - acc: 0.5067\n",
      "Epoch 16/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6983 - acc: 0.5037\n",
      "Epoch 17/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6980 - acc: 0.5052\n",
      "Epoch 18/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6978 - acc: 0.4978\n",
      "Epoch 19/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6975 - acc: 0.5022\n",
      "Epoch 20/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6973 - acc: 0.4978\n",
      "Epoch 21/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6971 - acc: 0.4978\n",
      "Epoch 22/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6968 - acc: 0.5007\n",
      "Epoch 23/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6966 - acc: 0.5052\n",
      "Epoch 24/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6964 - acc: 0.5067\n",
      "Epoch 25/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6962 - acc: 0.5097\n",
      "Epoch 26/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6961 - acc: 0.5097\n",
      "Epoch 27/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6958 - acc: 0.5112\n",
      "Epoch 28/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6957 - acc: 0.5112\n",
      "Epoch 29/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6955 - acc: 0.5082\n",
      "Epoch 30/60\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6953 - acc: 0.5082\n",
      "Epoch 31/60\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6951 - acc: 0.5097\n",
      "Epoch 32/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6950 - acc: 0.5082\n",
      "Epoch 33/60\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6948 - acc: 0.5112\n",
      " 64/667 [=>............................] - ETA: 8s - loss: 0.6827 - acc: 0.5938Epoch 34/60\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.6968 - acc: 0.4753\n",
      "Epoch 2/60\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6946 - acc: 0.5037\n",
      "Epoch 3/60\n",
      "667/667 [==============================] - 0s 60us/step - loss: 0.6947 - acc: 0.5052\n",
      "Epoch 35/60\n",
      "667/667 [==============================] - 0s 33us/step - loss: 0.6929 - acc: 0.5127\n",
      "Epoch 4/60\n",
      "667/667 [==============================] - 0s 78us/step - loss: 0.6915 - acc: 0.5277\n",
      "Epoch 5/60\n",
      "667/667 [==============================] - 0s 117us/step - loss: 0.6945 - acc: 0.5022\n",
      "Epoch 36/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6910 - acc: 0.5442\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6942 - acc: 0.5097\n",
      "Epoch 37/60\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6942 - acc: 0.5112\n",
      "Epoch 38/60\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6901 - acc: 0.4688Epoch 6/60\n",
      "667/667 [==============================] - 0s 30us/step - loss: 0.6941 - acc: 0.5082\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6988 - acc: 0.4219Epoch 39/60\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6902 - acc: 0.5517\n",
      "Epoch 7/60\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6939 - acc: 0.5067\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6830 - acc: 0.6094Epoch 40/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6937 - acc: 0.5082\n",
      "667/667 [==============================] - 0s 31us/step - loss: 0.6900 - acc: 0.5442\n",
      "Epoch 41/60\n",
      "Epoch 8/60\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6898 - acc: 0.5307\n",
      "Epoch 9/60\n",
      "667/667 [==============================] - 0s 52us/step - loss: 0.6936 - acc: 0.5082\n",
      "Epoch 42/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6896 - acc: 0.5322\n",
      "Epoch 10/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6895 - acc: 0.5322\n",
      "Epoch 11/60\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6934 - acc: 0.5037\n",
      "Epoch 43/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6894 - acc: 0.5217\n",
      "Epoch 12/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6933 - acc: 0.5022\n",
      "Epoch 44/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6894 - acc: 0.5187\n",
      "Epoch 13/60\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6932 - acc: 0.5037\n",
      "Epoch 45/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6896 - acc: 0.5112\n",
      "Epoch 14/60\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6931 - acc: 0.5037\n",
      "Epoch 46/60\n",
      "667/667 [==============================] - 0s 45us/step - loss: 0.6893 - acc: 0.5142\n",
      "128/666 [====>.........................] - ETA: 4s - loss: 0.6980 - acc: 0.5234Epoch 15/60\n",
      "666/666 [==============================] - 1s 2ms/step - loss: 0.7001 - acc: 0.5135\n",
      "Epoch 2/15\n",
      "667/667 [==============================] - 0s 55us/step - loss: 0.6930 - acc: 0.5007\n",
      "Epoch 47/60\n",
      "666/666 [==============================] - 0s 29us/step - loss: 0.6979 - acc: 0.5135\n",
      "Epoch 3/15\n",
      "667/667 [==============================] - 0s 38us/step - loss: 0.6892 - acc: 0.5247\n",
      "Epoch 16/60\n",
      "666/666 [==============================] - 0s 12us/step - loss: 0.6959 - acc: 0.5120\n",
      "Epoch 4/15\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6953 - acc: 0.5135\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6999 - acc: 0.4688Epoch 5/15\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6946 - acc: 0.5045\n",
      "667/667 [==============================] - 0s 36us/step - loss: 0.6929 - acc: 0.5052\n",
      "Epoch 6/15\n",
      "128/666 [====>.........................] - ETA: 0s - loss: 0.6926 - acc: 0.5391Epoch 48/60\n",
      "666/666 [==============================] - 0s 12us/step - loss: 0.6942 - acc: 0.4805\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6907 - acc: 0.5000Epoch 7/15\n",
      "667/667 [==============================] - 0s 76us/step - loss: 0.6892 - acc: 0.5232\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6940 - acc: 0.4745\n",
      "Epoch 8/15\n",
      "Epoch 17/60\n",
      "667/667 [==============================] - 0s 53us/step - loss: 0.6928 - acc: 0.5112\n",
      "Epoch 49/60\n",
      "667/667 [==============================] - 0s 34us/step - loss: 0.6891 - acc: 0.5202\n",
      "Epoch 18/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6927 - acc: 0.5112\n",
      "Epoch 50/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6891 - acc: 0.5142\n",
      "Epoch 19/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6926 - acc: 0.5112\n",
      "Epoch 51/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6890 - acc: 0.5247\n",
      "Epoch 20/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6926 - acc: 0.5172\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 52/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6890 - acc: 0.5202\n",
      "Epoch 21/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6924 - acc: 0.5202\n",
      "Epoch 53/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6890 - acc: 0.5157\n",
      "Epoch 22/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6924 - acc: 0.5187\n",
      "Epoch 54/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6889 - acc: 0.5097\n",
      "Epoch 23/60\n",
      "666/666 [==============================] - 0s 203us/step - loss: 0.6938 - acc: 0.4775\n",
      "Epoch 9/15\n",
      "666/666 [==============================] - 0s 28us/step - loss: 0.6937 - acc: 0.4865\n",
      "Epoch 10/15\n",
      "667/667 [==============================] - 0s 61us/step - loss: 0.6923 - acc: 0.5172\n",
      "128/666 [====>.........................] - ETA: 0s - loss: 0.6931 - acc: 0.4766Epoch 55/60\n",
      "667/667 [==============================] - 0s 65us/step - loss: 0.6890 - acc: 0.5157\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6861 - acc: 0.5781Epoch 24/60\n",
      "666/666 [==============================] - 0s 25us/step - loss: 0.6935 - acc: 0.4940\n",
      "Epoch 11/15\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6934 - acc: 0.5030\n",
      "Epoch 12/15\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6921 - acc: 0.5202\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6931 - acc: 0.4955\n",
      "Epoch 13/15\n",
      "666/666 [==============================] - 0s 12us/step - loss: 0.6929 - acc: 0.5045\n",
      "Epoch 56/60\n",
      "Epoch 14/15\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6889 - acc: 0.5202\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6928 - acc: 0.5165\n",
      "Epoch 15/15\n",
      "Epoch 25/60\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6927 - acc: 0.5090\n",
      "667/667 [==============================] - 0s 31us/step - loss: 0.6921 - acc: 0.5187\n",
      "Epoch 57/60\n",
      "667/667 [==============================] - 0s 50us/step - loss: 0.6889 - acc: 0.5067\n",
      "Epoch 26/60\n",
      "667/667 [==============================] - 0s 28us/step - loss: 0.6921 - acc: 0.5247\n",
      "Epoch 58/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6889 - acc: 0.5112\n",
      "Epoch 27/60\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6925 - acc: 0.5000Epoch 1/15\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6919 - acc: 0.5307\n",
      "Epoch 59/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6888 - acc: 0.5202\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.7013 - acc: 0.4531Epoch 28/60\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6918 - acc: 0.5322\n",
      "Epoch 60/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6889 - acc: 0.5142\n",
      " 64/667 [=>............................] - ETA: 0s - loss: 0.6946 - acc: 0.5156Epoch 29/60\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6919 - acc: 0.5307\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6887 - acc: 0.5247\n",
      "Epoch 30/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6887 - acc: 0.5217\n",
      "Epoch 31/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6887 - acc: 0.5217\n",
      "Epoch 32/60\n",
      "667/667 [==============================] - 0s 32us/step - loss: 0.6887 - acc: 0.5187\n",
      "Epoch 33/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6887 - acc: 0.5307\n",
      "Epoch 34/60\n",
      "334/334 [==============================] - 0s 626us/steps: 0.6905 - acc: 0.57\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6887 - acc: 0.5277\n",
      "Epoch 35/60\n",
      "666/666 [==============================] - 0s 9us/step\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6886 - acc: 0.5292\n",
      "Epoch 36/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6885 - acc: 0.5247\n",
      "Epoch 37/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6886 - acc: 0.5232\n",
      "Epoch 38/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6884 - acc: 0.5247\n",
      "Epoch 39/60\n",
      "333/333 [==============================] - 0s 594us/step\n",
      "667/667 [==============================] - 0s 14us/stepss: 0.6750 - acc: 0.59\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6884 - acc: 0.5187\n",
      "Epoch 40/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6884 - acc: 0.5232\n",
      "Epoch 41/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6884 - acc: 0.5247\n",
      "Epoch 42/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6883 - acc: 0.5277\n",
      "Epoch 43/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6883 - acc: 0.5262\n",
      "Epoch 44/60\n",
      "667/667 [==============================] - 0s 30us/step - loss: 0.6885 - acc: 0.5292\n",
      "Epoch 45/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6884 - acc: 0.5187\n",
      "Epoch 46/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6883 - acc: 0.5202\n",
      "Epoch 47/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6884 - acc: 0.5232\n",
      "Epoch 48/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6883 - acc: 0.5247\n",
      "Epoch 49/60\n",
      "667/667 [==============================] - 0s 42us/step - loss: 0.6881 - acc: 0.5217\n",
      "Epoch 50/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6881 - acc: 0.5262\n",
      "Epoch 51/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6882 - acc: 0.5277\n",
      "Epoch 52/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6881 - acc: 0.5247\n",
      "Epoch 53/60\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6881 - acc: 0.5307\n",
      "Epoch 54/60\n",
      "667/667 [==============================] - 0s 28us/step - loss: 0.6880 - acc: 0.5292\n",
      "Epoch 55/60\n",
      "667/667 [==============================] - 0s 99us/step - loss: 0.6880 - acc: 0.5247\n",
      "Epoch 56/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6880 - acc: 0.5232\n",
      "Epoch 57/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6881 - acc: 0.5217\n",
      "Epoch 58/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6880 - acc: 0.5262\n",
      "Epoch 59/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6880 - acc: 0.5232\n",
      "Epoch 60/60\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6879 - acc: 0.5247\n",
      "333/333 [==============================] - 0s 385us/step\n",
      "667/667 [==============================] - 0s 12us/step\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.7161 - acc: 0.5232\n",
      "Epoch 2/15\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.7107 - acc: 0.5232\n",
      "Epoch 3/15\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.7061 - acc: 0.5232\n",
      "Epoch 4/15\n",
      "667/667 [==============================] - 0s 32us/step - loss: 0.7035 - acc: 0.5262\n",
      "Epoch 5/15\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.7009 - acc: 0.5232\n",
      "Epoch 6/15\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6989 - acc: 0.5172\n",
      "Epoch 7/15\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6978 - acc: 0.5217\n",
      "Epoch 8/15\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6969 - acc: 0.5172\n",
      "Epoch 9/15\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6963 - acc: 0.5232\n",
      "Epoch 10/15\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6958 - acc: 0.5112\n",
      "Epoch 11/15\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6957 - acc: 0.5112\n",
      "Epoch 12/15\n",
      "Epoch 1/15\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6955 - acc: 0.5142\n",
      "Epoch 13/15\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6954 - acc: 0.5127\n",
      "Epoch 14/15\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6929 - acc: 0.5312Epoch 1/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6953 - acc: 0.5157\n",
      "Epoch 15/15\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6952 - acc: 0.5112\n",
      "333/333 [==============================] - 0s 395us/step\n",
      "667/667 [==============================] - 0s 9us/step\n",
      "Epoch 1/30\n",
      "128/667 [====>.........................] - ETA: 2s - loss: 0.7106 - acc: 0.5234Epoch 1/30\n",
      "667/667 [==============================] - 1s 927us/step - loss: 0.7021 - acc: 0.5247\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128/666 [====>.........................] - ETA: 2s - loss: 0.7121 - acc: 0.4922Epoch 2/15\n",
      "666/666 [==============================] - 1s 900us/step - loss: 0.6986 - acc: 0.5060\n",
      "Epoch 2/30\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6961 - acc: 0.5150\n",
      "Epoch 3/30\n",
      "667/667 [==============================] - 0s 53us/step - loss: 0.7001 - acc: 0.5247\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6945 - acc: 0.5345\n",
      "Epoch 4/30\n",
      "Epoch 3/15\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6932 - acc: 0.5420\n",
      "Epoch 5/30\n",
      "667/667 [==============================] - 0s 34us/step - loss: 0.6988 - acc: 0.5262\n",
      "Epoch 4/15\n",
      "666/666 [==============================] - 0s 21us/step - loss: 0.6928 - acc: 0.5390\n",
      "Epoch 6/30\n",
      "666/666 [==============================] - 0s 20us/step - loss: 0.6920 - acc: 0.5270\n",
      "667/667 [==============================] - 0s 30us/step - loss: 0.6973 - acc: 0.5247\n",
      "Epoch 7/30\n",
      "Epoch 5/15\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6964 - acc: 0.5262\n",
      "Epoch 6/15\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6957 - acc: 0.5292\n",
      "Epoch 7/15\n",
      "666/666 [==============================] - 0s 49us/step - loss: 0.6917 - acc: 0.5405\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.7021 - acc: 0.4375Epoch 8/30\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6952 - acc: 0.5292\n",
      "Epoch 8/15\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6947 - acc: 0.5292\n",
      "Epoch 9/15\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6945 - acc: 0.5307\n",
      "Epoch 10/15\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6942 - acc: 0.5307\n",
      "Epoch 11/15\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6939 - acc: 0.5292\n",
      "Epoch 12/15\n",
      "666/666 [==============================] - 0s 112us/step - loss: 0.6915 - acc: 0.5345\n",
      "Epoch 9/30\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6938 - acc: 0.5232\n",
      "Epoch 13/15\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6915 - acc: 0.5300\n",
      "Epoch 10/30\n",
      "667/667 [==============================] - 0s 28us/step - loss: 0.6936 - acc: 0.5202\n",
      "Epoch 14/15\n",
      "666/666 [==============================] - 0s 28us/step - loss: 0.6913 - acc: 0.5315\n",
      "Epoch 11/30\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6912 - acc: 0.5315\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 0s 34us/step - loss: 0.6935 - acc: 0.5202\n",
      "Epoch 15/15\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6911 - acc: 0.5315\n",
      "Epoch 13/30\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6910 - acc: 0.5255\n",
      "Epoch 14/30\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6934 - acc: 0.5217\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6907 - acc: 0.5375\n",
      "Epoch 15/30\n",
      "666/666 [==============================] - 0s 12us/step - loss: 0.6906 - acc: 0.5345\n",
      "Epoch 16/30\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6906 - acc: 0.5330\n",
      "Epoch 17/30\n",
      "666/666 [==============================] - 0s 37us/step - loss: 0.6904 - acc: 0.5405\n",
      "Epoch 18/30\n",
      "666/666 [==============================] - 0s 17us/step - loss: 0.6903 - acc: 0.5435\n",
      "Epoch 19/30\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6902 - acc: 0.5450\n",
      "Epoch 20/30\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6901 - acc: 0.5390\n",
      "Epoch 21/30\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6899 - acc: 0.5375\n",
      "Epoch 22/30\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6899 - acc: 0.5405\n",
      "Epoch 23/30\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6897 - acc: 0.5450\n",
      "Epoch 24/30\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6896 - acc: 0.5390\n",
      "Epoch 25/30\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6895 - acc: 0.5420\n",
      "Epoch 26/30\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6895 - acc: 0.5405\n",
      "Epoch 27/30\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6893 - acc: 0.5390\n",
      "Epoch 28/30\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6893 - acc: 0.5390\n",
      "Epoch 29/30\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6892 - acc: 0.5390\n",
      "Epoch 30/30\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6891 - acc: 0.5345\n",
      "333/333 [==============================] - 0s 742us/step\n",
      "667/667 [==============================] - 0s 10us/step\n",
      "667/667 [==============================] - 1s 1ms/step - loss: 0.7049 - acc: 0.5232\n",
      "Epoch 2/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.7026 - acc: 0.5232\n",
      "Epoch 3/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.7009 - acc: 0.5232\n",
      "Epoch 4/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6997 - acc: 0.5232\n",
      "Epoch 5/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6983 - acc: 0.5232\n",
      "Epoch 6/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6975 - acc: 0.5247\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6961 - acc: 0.5232\n",
      "Epoch 8/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6957 - acc: 0.5262\n",
      "Epoch 9/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6953 - acc: 0.5232\n",
      "Epoch 10/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6950 - acc: 0.5292\n",
      "Epoch 11/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6946 - acc: 0.5202\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6945 - acc: 0.5202\n",
      "Epoch 13/30\n",
      "334/334 [==============================] - 0s 817us/steps: 0.6997 - acc: 0.51\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6943 - acc: 0.5202\n",
      "Epoch 14/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6942 - acc: 0.5157\n",
      "Epoch 15/30\n",
      "666/666 [==============================] - 0s 43us/stepss: 0.6957 - acc: 0.50\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6941 - acc: 0.5097\n",
      "Epoch 16/30\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6941 - acc: 0.5112\n",
      "Epoch 17/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6940 - acc: 0.5007\n",
      "Epoch 18/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6939 - acc: 0.4963\n",
      "Epoch 19/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6939 - acc: 0.4828\n",
      "Epoch 20/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6939 - acc: 0.4798\n",
      "Epoch 21/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6938 - acc: 0.4798\n",
      "Epoch 22/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6938 - acc: 0.4813\n",
      "Epoch 23/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6937 - acc: 0.4843\n",
      "Epoch 24/30\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6937 - acc: 0.4798\n",
      "Epoch 25/30\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6936 - acc: 0.4843\n",
      "Epoch 26/30\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6935 - acc: 0.4858\n",
      "Epoch 27/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6935 - acc: 0.4843\n",
      "Epoch 28/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6934 - acc: 0.4993\n",
      "Epoch 29/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6934 - acc: 0.5067\n",
      "Epoch 30/30\n",
      "667/667 [==============================] - 1s 2ms/step - loss: 0.7057 - acc: 0.5247\n",
      "Epoch 2/30\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6933 - acc: 0.5097\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.7033 - acc: 0.5247\n",
      "Epoch 3/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.7016 - acc: 0.5247\n",
      "Epoch 4/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.7000 - acc: 0.5247\n",
      "Epoch 5/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6990 - acc: 0.5247\n",
      "Epoch 6/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6981 - acc: 0.5232\n",
      "Epoch 7/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6972 - acc: 0.5247\n",
      "Epoch 8/30\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6965 - acc: 0.5202\n",
      "Epoch 9/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6959 - acc: 0.5187\n",
      "Epoch 10/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6955 - acc: 0.5172\n",
      "Epoch 11/30\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6953 - acc: 0.5112\n",
      "Epoch 12/30\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6951 - acc: 0.5052\n",
      "Epoch 13/30\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6949 - acc: 0.5007\n",
      "Epoch 14/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6948 - acc: 0.5022\n",
      "Epoch 15/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6947 - acc: 0.4933\n",
      "Epoch 16/30\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6946 - acc: 0.4858\n",
      "Epoch 17/30\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6945 - acc: 0.4828\n",
      "Epoch 18/30\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6944 - acc: 0.4813\n",
      "Epoch 19/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6945 - acc: 0.4738\n",
      "Epoch 20/30\n",
      "667/667 [==============================] - 0s 29us/step - loss: 0.6943 - acc: 0.4738\n",
      "Epoch 21/30\n",
      "333/333 [==============================] - 0s 856us/step\n",
      "667/667 [==============================] - 0s 9us/step\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6943 - acc: 0.4738\n",
      "Epoch 22/30\n",
      "667/667 [==============================] - 0s 32us/step - loss: 0.6942 - acc: 0.4693\n",
      "Epoch 23/30\n",
      "667/667 [==============================] - 0s 23us/step - loss: 0.6941 - acc: 0.4783\n",
      "Epoch 24/30\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6941 - acc: 0.4798\n",
      "Epoch 25/30\n",
      "667/667 [==============================] - 0s 60us/step - loss: 0.6940 - acc: 0.4768\n",
      "Epoch 26/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6939 - acc: 0.4783\n",
      "Epoch 27/30\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6939 - acc: 0.4948\n",
      "Epoch 28/30\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6938 - acc: 0.4948\n",
      "Epoch 29/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6937 - acc: 0.4948\n",
      "Epoch 30/30\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6937 - acc: 0.5022\n",
      "Epoch 1/60\n",
      "333/333 [==============================] - 0s 593us/step\n",
      "667/667 [==============================] - 0s 41us/step\n",
      "Epoch 1/60\n",
      "Epoch 1/60\n",
      "666/666 [==============================] - 1s 927us/step - loss: 0.7267 - acc: 0.5120\n",
      "Epoch 2/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.7201 - acc: 0.5120\n",
      "Epoch 3/60\n",
      "666/666 [==============================] - 0s 26us/step - loss: 0.7151 - acc: 0.5120\n",
      "Epoch 4/60\n",
      "666/666 [==============================] - 0s 19us/step - loss: 0.7108 - acc: 0.5135\n",
      "Epoch 5/60\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.7066 - acc: 0.5150\n",
      "Epoch 6/60\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.7038 - acc: 0.5135\n",
      "Epoch 7/60\n",
      "666/666 [==============================] - 0s 17us/step - loss: 0.7013 - acc: 0.5135\n",
      "Epoch 8/60\n",
      "666/666 [==============================] - 0s 23us/step - loss: 0.6999 - acc: 0.4985\n",
      "Epoch 9/60\n",
      "667/667 [==============================] - 1s 941us/step - loss: 0.6996 - acc: 0.5097\n",
      "Epoch 2/60\n",
      "666/666 [==============================] - 0s 25us/step - loss: 0.6985 - acc: 0.4835\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6980 - acc: 0.5142\n",
      "Epoch 10/60\n",
      "Epoch 3/60\n",
      "667/667 [==============================] - 0s 26us/step - loss: 0.6969 - acc: 0.4993\n",
      "Epoch 4/60\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6957 - acc: 0.4993\n",
      "Epoch 5/60\n",
      "666/666 [==============================] - 0s 59us/step - loss: 0.6980 - acc: 0.4760\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6953 - acc: 0.4963\n",
      "Epoch 6/60\n",
      "Epoch 11/60\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6948 - acc: 0.5037\n",
      "Epoch 7/60\n",
      "666/666 [==============================] - 0s 52us/step - loss: 0.6974 - acc: 0.4640\n",
      "Epoch 12/60\n",
      "667/667 [==============================] - 0s 55us/step - loss: 0.6945 - acc: 0.5037\n",
      "Epoch 8/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6970 - acc: 0.4520\n",
      "Epoch 13/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6943 - acc: 0.4978\n",
      "Epoch 9/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6966 - acc: 0.4610\n",
      "Epoch 14/60\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6940 - acc: 0.4948\n",
      "Epoch 10/60\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6964 - acc: 0.4640\n",
      "Epoch 15/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6938 - acc: 0.5082\n",
      "Epoch 11/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6961 - acc: 0.4730\n",
      "Epoch 16/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6937 - acc: 0.5112\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6959 - acc: 0.4670\n",
      "Epoch 12/60\n",
      "Epoch 17/60\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6958 - acc: 0.4670\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6935 - acc: 0.5157\n",
      "Epoch 18/60\n",
      "Epoch 13/60\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6956 - acc: 0.4820\n",
      "Epoch 19/60\n",
      "667/667 [==============================] - 0s 25us/step - loss: 0.6934 - acc: 0.5142\n",
      "Epoch 14/60\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6934 - acc: 0.5097\n",
      "Epoch 15/60\n",
      "666/666 [==============================] - 0s 39us/step - loss: 0.6953 - acc: 0.4865\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.7066 - acc: 0.4453Epoch 20/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6932 - acc: 0.5082\n",
      "Epoch 16/60\n",
      "666/666 [==============================] - 0s 26us/step - loss: 0.6951 - acc: 0.4850\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6931 - acc: 0.5112\n",
      "Epoch 21/60\n",
      "Epoch 17/60\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6949 - acc: 0.4805\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6932 - acc: 0.5187\n",
      "Epoch 22/60\n",
      "Epoch 18/60\n",
      "666/666 [==============================] - 0s 13us/step - loss: 0.6946 - acc: 0.4790\n",
      "Epoch 23/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6930 - acc: 0.5187\n",
      "Epoch 19/60\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6944 - acc: 0.4865\n",
      "Epoch 24/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6929 - acc: 0.5097\n",
      "Epoch 20/60\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6942 - acc: 0.4850\n",
      "Epoch 25/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6928 - acc: 0.5157\n",
      "Epoch 21/60\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6939 - acc: 0.4940\n",
      "Epoch 26/60\n",
      "666/666 [==============================] - 0s 17us/step - loss: 0.6937 - acc: 0.4985\n",
      "Epoch 27/60\n",
      "667/667 [==============================] - 0s 32us/step - loss: 0.6928 - acc: 0.5187\n",
      "Epoch 22/60\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6935 - acc: 0.4940\n",
      "Epoch 28/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6927 - acc: 0.5217\n",
      "Epoch 23/60\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6933 - acc: 0.5105\n",
      "Epoch 29/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6925 - acc: 0.5097\n",
      "Epoch 24/60\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6931 - acc: 0.5105\n",
      "Epoch 30/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6924 - acc: 0.5217\n",
      "128/666 [====>.........................] - ETA: 0s - loss: 0.6936 - acc: 0.5000Epoch 25/60\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6929 - acc: 0.5120\n",
      "Epoch 31/60\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6921 - acc: 0.5187\n",
      "128/666 [====>.........................] - ETA: 0s - loss: 0.6936 - acc: 0.4609Epoch 26/60\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "666/666 [==============================] - 0s 15us/step - loss: 0.6928 - acc: 0.5180\n",
      "Epoch 32/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6921 - acc: 0.5202\n",
      "128/666 [====>.........................] - ETA: 0s - loss: 0.6907 - acc: 0.5938Epoch 27/60\n",
      "666/666 [==============================] - 0s 17us/step - loss: 0.6927 - acc: 0.5360\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6919 - acc: 0.5172\n",
      "Epoch 33/60\n",
      "Epoch 28/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6918 - acc: 0.5262\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6926 - acc: 0.5270\n",
      "Epoch 29/60\n",
      "Epoch 34/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6918 - acc: 0.5292\n",
      "Epoch 30/60\n",
      "666/666 [==============================] - 0s 25us/step - loss: 0.6926 - acc: 0.5330\n",
      "Epoch 35/60\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6917 - acc: 0.5322\n",
      "Epoch 31/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6924 - acc: 0.5330\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6916 - acc: 0.5307\n",
      "Epoch 36/60\n",
      "Epoch 32/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6915 - acc: 0.5367\n",
      "Epoch 33/60\n",
      "666/666 [==============================] - 0s 30us/step - loss: 0.6920 - acc: 0.5345\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6915 - acc: 0.5412\n",
      "Epoch 37/60\n",
      "Epoch 34/60\n",
      "667/667 [==============================] - 0s 12us/step - loss: 0.6914 - acc: 0.5412\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6919 - acc: 0.5450\n",
      "Epoch 35/60\n",
      "Epoch 38/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6914 - acc: 0.5382\n",
      "Epoch 36/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6917 - acc: 0.5511\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6893 - acc: 0.5469Epoch 39/60\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6912 - acc: 0.5382\n",
      "Epoch 37/60\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6916 - acc: 0.5526\n",
      "Epoch 40/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6911 - acc: 0.5427\n",
      "Epoch 38/60\n",
      "666/666 [==============================] - 0s 14us/step - loss: 0.6913 - acc: 0.5330\n",
      "Epoch 41/60\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6911 - acc: 0.5547\n",
      "Epoch 39/60\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6912 - acc: 0.5165\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6906 - acc: 0.5938Epoch 42/60\n",
      "667/667 [==============================] - 1s 2ms/step - loss: 0.6960 - acc: 0.4933\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6910 - acc: 0.5517\n",
      "Epoch 2/60\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6912 - acc: 0.5165\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6892 - acc: 0.5156Epoch 43/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6955 - acc: 0.5022\n",
      "Epoch 3/60\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6922 - acc: 0.5469Epoch 40/60\n",
      "666/666 [==============================] - 0s 26us/step - loss: 0.6909 - acc: 0.5180\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6952 - acc: 0.5037\n",
      "Epoch 44/60\n",
      "Epoch 4/60\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6949 - acc: 0.5037\n",
      "Epoch 5/60\n",
      "666/666 [==============================] - 0s 20us/step - loss: 0.6910 - acc: 0.5180\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6959 - acc: 0.5234Epoch 45/60\n",
      "667/667 [==============================] - 0s 28us/step - loss: 0.6910 - acc: 0.5412\n",
      "128/666 [====>.........................] - ETA: 0s - loss: 0.6948 - acc: 0.4531Epoch 41/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6945 - acc: 0.5082\n",
      "Epoch 6/60\n",
      "666/666 [==============================] - 0s 22us/step - loss: 0.6907 - acc: 0.5330\n",
      "Epoch 46/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6944 - acc: 0.5082\n",
      "Epoch 7/60\n",
      "667/667 [==============================] - 0s 29us/step - loss: 0.6909 - acc: 0.5457\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6941 - acc: 0.5082\n",
      "Epoch 42/60\n",
      "666/666 [==============================] - 0s 20us/step - loss: 0.6907 - acc: 0.5375\n",
      "Epoch 8/60\n",
      "Epoch 47/60\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6938 - acc: 0.5127\n",
      "Epoch 9/60\n",
      "666/666 [==============================] - 0s 20us/step - loss: 0.6906 - acc: 0.5390\n",
      "Epoch 48/60\n",
      "667/667 [==============================] - 0s 28us/step - loss: 0.6908 - acc: 0.5457\n",
      "Epoch 43/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6936 - acc: 0.5052\n",
      "Epoch 10/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6907 - acc: 0.5457\n",
      "Epoch 44/60\n",
      "666/666 [==============================] - 0s 24us/step - loss: 0.6905 - acc: 0.5390\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6916 - acc: 0.5312Epoch 49/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6906 - acc: 0.5367\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6990 - acc: 0.4609Epoch 45/60\n",
      "666/666 [==============================] - 0s 19us/step - loss: 0.6904 - acc: 0.5420\n",
      "Epoch 50/60\n",
      "667/667 [==============================] - 0s 37us/step - loss: 0.6936 - acc: 0.5097\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6906 - acc: 0.5382\n",
      "Epoch 11/60\n",
      "Epoch 46/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6902 - acc: 0.5330\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6936 - acc: 0.5156Epoch 51/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6905 - acc: 0.5262\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6933 - acc: 0.5112\n",
      "Epoch 12/60\n",
      "Epoch 47/60\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6901 - acc: 0.5315\n",
      "Epoch 52/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6904 - acc: 0.5247\n",
      "Epoch 48/60\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6931 - acc: 0.5307\n",
      "Epoch 13/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6900 - acc: 0.5300\n",
      "Epoch 53/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6904 - acc: 0.5157\n",
      "Epoch 49/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6929 - acc: 0.5292\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6947 - acc: 0.5000Epoch 14/60\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6898 - acc: 0.5315\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6901 - acc: 0.5547Epoch 54/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6905 - acc: 0.5157\n",
      "128/666 [====>.........................] - ETA: 0s - loss: 0.6917 - acc: 0.5234Epoch 50/60\n",
      "666/666 [==============================] - 0s 17us/step - loss: 0.6897 - acc: 0.5360\n",
      "Epoch 55/60\n",
      "667/667 [==============================] - 0s 22us/step - loss: 0.6928 - acc: 0.5397\n",
      "Epoch 15/60\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6905 - acc: 0.5187\n",
      "128/666 [====>.........................] - ETA: 0s - loss: 0.6923 - acc: 0.5156Epoch 51/60\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6926 - acc: 0.5352\n",
      "Epoch 16/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6904 - acc: 0.5202\n",
      "666/666 [==============================] - 0s 31us/step - loss: 0.6896 - acc: 0.5375\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6925 - acc: 0.5322\n",
      "Epoch 52/60\n",
      "Epoch 17/60\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6906 - acc: 0.4609Epoch 56/60\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6924 - acc: 0.5307\n",
      "Epoch 18/60\n",
      "667/667 [==============================] - 0s 27us/step - loss: 0.6903 - acc: 0.5157\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6941 - acc: 0.5312Epoch 53/60\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6922 - acc: 0.5337\n",
      "Epoch 19/60\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6920 - acc: 0.5352\n",
      "667/667 [==============================] - 0s 34us/step - loss: 0.6904 - acc: 0.5187\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/60\n",
      "Epoch 54/60\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6919 - acc: 0.5337\n",
      "Epoch 21/60\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6919 - acc: 0.5277\n",
      "Epoch 22/60\n",
      "667/667 [==============================] - 0s 44us/step - loss: 0.6903 - acc: 0.5157\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6971 - acc: 0.4766Epoch 55/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6918 - acc: 0.5337\n",
      "666/666 [==============================] - 0s 110us/step - loss: 0.6894 - acc: 0.5405\n",
      "Epoch 57/60\n",
      "Epoch 23/60\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6902 - acc: 0.5157\n",
      "666/666 [==============================] - 0s 18us/step - loss: 0.6893 - acc: 0.5375\n",
      "Epoch 56/60\n",
      "Epoch 58/60\n",
      "667/667 [==============================] - 0s 33us/step - loss: 0.6918 - acc: 0.5322\n",
      "666/666 [==============================] - 0s 15us/step - loss: 0.6892 - acc: 0.5375\n",
      "Epoch 24/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6902 - acc: 0.5172\n",
      "Epoch 59/60\n",
      "Epoch 57/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6892 - acc: 0.5390\n",
      "667/667 [==============================] - 0s 17us/step - loss: 0.6901 - acc: 0.5142\n",
      "Epoch 60/60\n",
      "Epoch 58/60\n",
      "667/667 [==============================] - 0s 21us/step - loss: 0.6916 - acc: 0.5352\n",
      "128/667 [====>.........................] - ETA: 0s - loss: 0.6880 - acc: 0.5000Epoch 25/60\n",
      "666/666 [==============================] - 0s 16us/step - loss: 0.6890 - acc: 0.5375\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6901 - acc: 0.5172\n",
      "Epoch 59/60\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6916 - acc: 0.5352\n",
      "Epoch 26/60\n",
      "667/667 [==============================] - 0s 19us/step - loss: 0.6901 - acc: 0.5202\n",
      "Epoch 60/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6915 - acc: 0.5382\n",
      "Epoch 27/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6902 - acc: 0.5172\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6913 - acc: 0.5382\n",
      "Epoch 28/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6912 - acc: 0.5397\n",
      "Epoch 29/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6911 - acc: 0.5367\n",
      "Epoch 30/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6911 - acc: 0.5382\n",
      "Epoch 31/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6910 - acc: 0.5397\n",
      "Epoch 32/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6910 - acc: 0.5382\n",
      "Epoch 33/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6909 - acc: 0.5382\n",
      "Epoch 34/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6908 - acc: 0.5367\n",
      "Epoch 35/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6908 - acc: 0.5367\n",
      "Epoch 36/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6908 - acc: 0.5337\n",
      "Epoch 37/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6907 - acc: 0.5352\n",
      "Epoch 38/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6907 - acc: 0.5367\n",
      "Epoch 39/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6907 - acc: 0.5367\n",
      "Epoch 40/60\n",
      "667/667 [==============================] - 0s 13us/step - loss: 0.6906 - acc: 0.5337\n",
      "Epoch 41/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6906 - acc: 0.5382\n",
      "Epoch 42/60\n",
      "334/334 [==============================] - 0s 735us/steps: 0.6932 - acc: 0.50\n",
      "667/667 [==============================] - 0s 29us/step - loss: 0.6906 - acc: 0.5412\n",
      "Epoch 43/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6905 - acc: 0.5457\n",
      "Epoch 44/60\n",
      "666/666 [==============================] - 0s 42us/step\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6905 - acc: 0.5457\n",
      "Epoch 45/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6905 - acc: 0.5442\n",
      "Epoch 46/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6904 - acc: 0.5442\n",
      "Epoch 47/60\n",
      "333/333 [==============================] - 0s 878us/steps: 0.6894 - acc: 0.51\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6904 - acc: 0.5442\n",
      "Epoch 48/60\n",
      "667/667 [==============================] - 0s 9us/step\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6904 - acc: 0.5442\n",
      "Epoch 49/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6905 - acc: 0.5442\n",
      "Epoch 50/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6904 - acc: 0.5457\n",
      "Epoch 51/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6904 - acc: 0.5457\n",
      "Epoch 52/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6904 - acc: 0.5442\n",
      "Epoch 53/60\n",
      "667/667 [==============================] - 0s 24us/step - loss: 0.6903 - acc: 0.5457\n",
      "Epoch 54/60\n",
      "667/667 [==============================] - 0s 18us/step - loss: 0.6903 - acc: 0.5457\n",
      "Epoch 55/60\n",
      "667/667 [==============================] - 0s 20us/step - loss: 0.6903 - acc: 0.5442\n",
      "Epoch 56/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6902 - acc: 0.5397\n",
      "Epoch 57/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6901 - acc: 0.5427\n",
      "Epoch 58/60\n",
      "667/667 [==============================] - 0s 15us/step - loss: 0.6901 - acc: 0.5427\n",
      "Epoch 59/60\n",
      "667/667 [==============================] - 0s 14us/step - loss: 0.6901 - acc: 0.5442\n",
      "Epoch 60/60\n",
      "667/667 [==============================] - 0s 16us/step - loss: 0.6900 - acc: 0.5427\n",
      "333/333 [==============================] - 0s 377us/step\n",
      "667/667 [==============================] - 0s 7us/step\n",
      "Epoch 1/60\n",
      "1000/1000 [==============================] - 0s 222us/step - loss: 0.6955 - acc: 0.5160\n",
      "Epoch 2/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6948 - acc: 0.5270\n",
      "Epoch 3/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6944 - acc: 0.5180\n",
      "Epoch 4/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6940 - acc: 0.5130\n",
      "Epoch 5/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6938 - acc: 0.5120\n",
      "Epoch 6/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6933 - acc: 0.5150\n",
      "Epoch 7/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6930 - acc: 0.5190\n",
      "Epoch 8/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6928 - acc: 0.5130\n",
      "Epoch 9/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6926 - acc: 0.5160\n",
      "Epoch 10/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6922 - acc: 0.5110\n",
      "Epoch 11/60\n",
      "1000/1000 [==============================] - 0s 19us/step - loss: 0.6921 - acc: 0.5140\n",
      "Epoch 12/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6919 - acc: 0.5130\n",
      "Epoch 13/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6916 - acc: 0.5150\n",
      "Epoch 14/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6915 - acc: 0.5100\n",
      "Epoch 15/60\n",
      "1000/1000 [==============================] - 0s 18us/step - loss: 0.6913 - acc: 0.5190\n",
      "Epoch 16/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6911 - acc: 0.5210\n",
      "Epoch 17/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6909 - acc: 0.5190\n",
      "Epoch 18/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6909 - acc: 0.5180\n",
      "Epoch 19/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6907 - acc: 0.5220\n",
      "Epoch 20/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6906 - acc: 0.5190\n",
      "Epoch 21/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6905 - acc: 0.5210\n",
      "Epoch 22/60\n",
      "1000/1000 [==============================] - 0s 18us/step - loss: 0.6904 - acc: 0.5160\n",
      "Epoch 23/60\n",
      "1000/1000 [==============================] - 0s 20us/step - loss: 0.6903 - acc: 0.5170\n",
      "Epoch 24/60\n",
      "1000/1000 [==============================] - 0s 21us/step - loss: 0.6904 - acc: 0.5210\n",
      "Epoch 25/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6902 - acc: 0.5200\n",
      "Epoch 26/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6901 - acc: 0.5180\n",
      "Epoch 27/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6902 - acc: 0.5240\n",
      "Epoch 28/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6899 - acc: 0.5230\n",
      "Epoch 29/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6899 - acc: 0.5230\n",
      "Epoch 30/60\n",
      "1000/1000 [==============================] - 0s 19us/step - loss: 0.6898 - acc: 0.5260\n",
      "Epoch 31/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6897 - acc: 0.5310\n",
      "Epoch 32/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6897 - acc: 0.5220\n",
      "Epoch 33/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6897 - acc: 0.5340\n",
      "Epoch 34/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6895 - acc: 0.5310\n",
      "Epoch 35/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6895 - acc: 0.5290\n",
      "Epoch 36/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6895 - acc: 0.5250\n",
      "Epoch 37/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6893 - acc: 0.5280\n",
      "Epoch 38/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6893 - acc: 0.5320\n",
      "Epoch 39/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6893 - acc: 0.5300\n",
      "Epoch 40/60\n",
      "1000/1000 [==============================] - 0s 17us/step - loss: 0.6892 - acc: 0.5340\n",
      "Epoch 41/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6891 - acc: 0.5340\n",
      "Epoch 42/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6891 - acc: 0.5300\n",
      "Epoch 43/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6890 - acc: 0.5290\n",
      "Epoch 44/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6891 - acc: 0.5260\n",
      "Epoch 45/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6889 - acc: 0.5300\n",
      "Epoch 46/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6888 - acc: 0.5250\n",
      "Epoch 47/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6889 - acc: 0.5260\n",
      "Epoch 48/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6888 - acc: 0.5270\n",
      "Epoch 49/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6888 - acc: 0.5250\n",
      "Epoch 50/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6887 - acc: 0.5270\n",
      "Epoch 51/60\n",
      "1000/1000 [==============================] - 0s 18us/step - loss: 0.6887 - acc: 0.5310\n",
      "Epoch 52/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6886 - acc: 0.5300\n",
      "Epoch 53/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6887 - acc: 0.5270\n",
      "Epoch 54/60\n",
      "1000/1000 [==============================] - 0s 16us/step - loss: 0.6886 - acc: 0.5260\n",
      "Epoch 55/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6885 - acc: 0.5300\n",
      "Epoch 56/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6885 - acc: 0.5350\n",
      "Epoch 57/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6884 - acc: 0.5330\n",
      "Epoch 58/60\n",
      "1000/1000 [==============================] - 0s 15us/step - loss: 0.6884 - acc: 0.5320\n",
      "Epoch 59/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6887 - acc: 0.5330\n",
      "Epoch 60/60\n",
      "1000/1000 [==============================] - 0s 14us/step - loss: 0.6885 - acc: 0.5350\n"
     ]
    }
   ],
   "source": [
    "from keras import Sequential\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "\n",
    "#Generate dummy data for 3 features and 1000 samples\n",
    "x_train = np.random.random((1000, 3))\n",
    "\n",
    "#Generate dummy results for 1000 samples: 1 or 0\n",
    "y_train = np.random.randint(2, size=(1000, 1))\n",
    "\n",
    "#Create a python function that returns a compiled DNN model\n",
    "def create_dnn_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(12, input_dim=3, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model \n",
    "\n",
    "#Use Keras wrapper to package the model as an sklearn object\n",
    "model = KerasClassifier(build_fn=create_dnn_model)\n",
    "\n",
    "# define the grid search parameters\n",
    "batch_size = [32,64,128]\n",
    "epochs = [15, 30, 60]\n",
    "\n",
    "#Create a list with the parameters\n",
    "param_grid =  {\"batch_size\":batch_size, \"epochs\":epochs}\n",
    "#Invoke the grid search method with the list of hyperparameters\n",
    "grid_model = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)\n",
    "#Train the model\n",
    "grid_model.fit(x_train, y_train)\n",
    "\n",
    "#Extract the best model grid search\n",
    "best_model = grid_model.best_estimator_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the best model based on validation accuracy during training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "filepath = \"ModelWeights-{epoch:.2f}-{val_acc:.2f}.hdf5\"\n",
    "checkpoint = ModelCheckpoint(filepath, save_best_only=True, monitor=\"val_acc\")\n",
    "\n",
    "model.fit(x_train, y_train, callbacks=[checkpoint],epochs=100, batch_size=64)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the model (weights and structure) after training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "#Train a model for defined number of epochs\n",
    "model.fit(x_train, y_train, epochs=100, batch_size=64)\n",
    "\n",
    "# Saves the entire model into a file named as  'dnn_model.h5'\n",
    "model.save('dnn_model.h5')  \n",
    "\n",
    "# Later, (maybe another day), you can load the trained model for prediction.\n",
    "model = load_model('dnn_model.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(<scipy.stats._distn_infrastructure.rv_frozen object at 0x1a13cb5358>,\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import randint as sp_randint\n",
    "from keras import Sequential\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "\n",
    "#Generate dummy data for 3 features and 1000 samples\n",
    "x_train = np.random.random((1000, 3))\n",
    "\n",
    "#Generate dummy results for 1000 samples: 1 or 0\n",
    "y_train = np.random.randint(2, size=(1000, 1))\n",
    "\n",
    "#Create a python function that returns a compiled DNN model\n",
    "def create_dnn_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(12, input_dim=3, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model \n",
    "\n",
    "#Use Keras wrapper to package the model as an sklearn object\n",
    "model = KerasClassifier(build_fn=create_dnn_model)\n",
    "\n",
    "# define the grid search parameters\n",
    "batch_size = [32,64,128]\n",
    "epochs = [15, 30, 60]\n",
    "\n",
    "\n",
    "#Create a list with the parameters\n",
    "param_grid =  {\"batch_size\":batch_size, \"epochs\":epochs}\n",
    "#Invoke the grid search method with the list of hyperparameters\n",
    "grid_model = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)\n",
    "#Train the model\n",
    "grid_model.fit(x_train, y_train)\n",
    "\n",
    "#Extract the best model grid search\n",
    "best_model = grid_model.best_estimator_\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
